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Heuristics - Science topic

Heuristic refers to experience-based techniques for problem solving, learning, and discovery. Where an exhaustive search is impractical, heuristic methods are used to speed up the process of finding a satisfactory solution.
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I'm reading a paper and I couldn't understand how to read exactly this plot. in the paper they say that it shows the belief distributions that result from using weighted A* with a weight of 2 and LSS-LRTA* for the sampling. the generated beliefs are very similar, with only minor differences for large heuristic values where fewer samples have been observed.
can I know the name of this kind of plots too?
thank you
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Steftcho P. Dokov Thank you so much!
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Hi!
I'm working on a phylogenetic inference (molecular) with 205 taxa and 5350 characters (7 different genes).
I've ever made a phylogeny thanks to a supermatrix. There were some polytomies. The problem is that some have lacks of sequences. Thus, I'd like to make a supertree to compare and see if there will be polytomies again or not.
This way, I inferred trees for each genes in ML with IQtree2. Then, I used Clann to make a matrix as a MRP (Matrix Representation with Parsimony) with 7 source trees. Next, I used PAUP to start a heuristic search (in parsimony) with these command lines in my nexus file (as Clann suggested) :
begin paup;
set increase=auto notifybeep=no errorbeep=no;
hs nreps=10 swap=tbr addseq=random;
showtrees;
savetrees FILE=MRP.tree Format=nexus treeWts=yes Append=no replace=yes;
quit;
end;
However, the search is working for hours (since 8:00 pm, yesterday) and it doesn't stop. More than 10 billion rearrangement were tried 1 721 900 trees are already saved, whereas it's only the first replicate. The analysis tells that the best tree is the tree n°3088, but the heuristic search continues.
Regarding the number of taxa and characters, is it normal that it take so much time?
Is there an error in my command lines?
It is the first time I try to build a supertree.
Can you help me?
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I am interested in the use of Extreme Value Theory (EVT) to estimate global optima of optimization problems (using heuristic and metaheuristic algorithms), however, it is a bit difficult to find them since the use of EVT is not usually the main objective of the studies. Could you help me by sharing articles where this procedure is used? Thank you in advance.
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Bettinger, P., J. Sessions, and K. Boston. 2009. A review of the status and use of validation procedures for heuristics used in forest planning. Mathematical and Computational Forestry & Natural-Resource Sciences. 1(1): 26-37.
Bettinger, P., J. Sessions, and K.N. Johnson. 1998. Ensuring the compatibility of aquatic habitat and commodity production goals in eastern Oregon with a Tabu search procedure. Forest Science. 44(1): 96-112.
Boston, K. and P. Bettinger. 1999. An analysis of Monte Carlo integer programming, simulated annealing, and tabu search heuristics for solving spatial harvest scheduling problems. Forest Science. 45(2): 292-301.
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I wonder if there are some advice more senior researchers here can share on how to identify interesting topics that are likely to interest reviewers and editors particularly in a hermeneutic social science approach.
Your inputs will be highly appreciated. Thank you
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In my personal experience, I think it takes three things: approaching researchers with vision, not being afraid to innovate, and looking for topics that have a large number of recent publications. By combining those three you can create papers that are of great interest to reviewers and that really bring something of value to the area of study. My first paper was published in a first quartile journal and those were, in my opinion, the reasons that led me to that. I hope it has helped you.
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Having come to realize the limitations that metaheuristics have by dint of the NFL theorem, I came across this interesting field of hyper-heuristics (heuristics searching for heuristics) and read a couple of papers on the topic. I was wondering whether any of you can give me a list of recommended books for further learning. Online video courses will also be greatly helpful. Thanks in advance.
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Much appreciation, Prof. Mohamed-Mourad Lafifi
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I have already gone in deep with the GP initialization method and I found that there are some traditional methods that ensure the diversity in the population on the initialization phase of the GP process like RHH, Grow, Full ..etc. my question is if there some other method that ensures the same purpose with those ones or if there some hybridization with other heuristics?
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Hello, I took a look at the article and on the PhD thesis but I guess it talks about Genetic algorithms using a numerical representation of the population.
I'm interested in Genetic programming Tree Representation.
Thank you Rohail Gulbaz
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As we know, heuristic algorithms are effective way to search substitution-box (S-box) which has high nonlinearity. Lots of nonlinearity calculations of S-box are needed in these process which make the speed of nonlinearity calculation quite important. So, what is the approximate minimun time to calculate the nonlinearity of an 8x8 S-box (On Intel Core i7 CPU for example)? And what is the key points in programming?
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On heuristic algorithms, using Local Search approach is a fast way to search S-boxes with high Non-Linearity. Please check this research
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I have a multi-objective optimization with the following properties:
Objective function: thee minimization objective functions) two non-linear functions and one linear function
Decision variable: two real variables (Bounded)
Constraint: three linear constraint (two bounding constraint and one relationship constraint)
Problem type: non-convex
Solution required: Global optimum
I have used two heuristic algorithms to solve the problem NSGA-II and NSGA-III.
I have performed NSGA-II and NSGA-III for the following instances (population size, number of generations, maximum number of functional evaluations(i.e. pop size x no. of gen)): (100,10,1000), (100,50,5000),(100,100,10000), (500, 10, 1000), (500, 50, 25000), and (500,100,50000).
My observations:
Hypervolume increases with increase in number of functional evaluations. However, for a given population size, as the number of generation increases the hypervolume reduces. Which I think should rather increase. Why am I getting such an answer?
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Greetings to you all.
Please how can I find MATLAB code for Accelerated Particle Swarm Optimization algorithm for tuning PID controller.
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Sometimes I have found an inconsistency gives a helpful clue of how to improve a theoretical investigation. Early on I viewed mistakes as hurdles. I still think they are hurdles but have many times found them to be helpful. My view is that it encourages persistence to know that mistakes are part of the process of figuring things out. Are there articles about the role of making mistakes in theoretical physics?
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Dear Robert Shour,
I have found that after some mathematical derivation or during logical conclusion of some ideas, I make mistakes sometimes.
Later, further thinking over that matter, when the mistakes are found and corrected, I get much alert and the mistakes give me the idea of what problem was there in my conception. Overall, these helps a lot.
Thanks
N Das
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Everyone knows that optimization problems can be solved by mathematical programming techniques, whether they are (linear - non-linear - mixture - ...) and also can be solved by heuristic techniques. Now which are better, mathematical programming techniques or metaheuristic techniques?
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Joao Luiz Junho Pereira I think you will have a very, very hard time convincing any mathematical programming expert that you are right ... and for a very simple reason: that you are wrong. :-)
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Recently I come across 2 article bearing uncanny similarities.
After some investigation, I suspect that this article:
might have been plagiarized from this one:
My question is: Should the below findings suggest any suspicion that one of these articles might have been plagiarized?
The percentage of similarity is not very high, around 150 - 200 words over 14.000 words. But there are long phrases (sometimes as long as 16 consecutive words) appearing in both articles without any quotation marks. There were neither acknowledgement nor citation of each other.
Below are some of the similarities that I found:
Nahon (2008) Abstract: Gatekeeping theories have been a popular heuristic for describing information control for years, but none have attained a full theoretical status in the context of networks.
Mitchell et al. (1997) Abstract: Stakeholder theory has been a popular heuristic for describing the management environment for years, but it has not attained full theoretical status.
Nahon (2008), p. 1501: First, each attribute is a variable, not a steady state, and can change for any particular relationship among gatekeepers or during gatekeeper–gated relationships. p. 1501
Mitchell et al. (1997) p. 868: First, each attribute is a variable, not a steady state, and can change for any particular entity or stakeholder-manager relationship.
Nahon (2008), p. 1493: Salience refers to the degree to which gatekeepers give priority to competing gated claims.
Mitchell et al. (1997), p. 854: stakeholder salience - the degree to which managers give priority to competing stakeholder claims
Nahon (2008), p. 1493: However, as popular as the term has become and as richly descriptive as it is, there is little agreement among the different fields on its meaning and a lack of full theoretical status.
Mitchell et al. (1997), p. 853: Yet, as popular as the term has become and as richly descriptive as it is, there is no agreement on what Freeman (1994) calls "The Principle of Who or What Really Counts."
Nahon (2008), p. 1506: While static maps of gatekeepers are heuristically useful if the intent is to raise consciousness about “who or what really counts” or to specify a stakeholder configuration at a particular context and time, one should remember that this is a simplification of reality.
Mitchell et al. (1997), p. 879: Static maps of a firm's stakeholder environment are heuristically useful if the intent is to raise consciousness about "Who or What Really Counts" to managers or to specify the stakeholder configuration at a particular time point.
I tried to contact one author and they replied that the other article had been "an inspiration" for them and admit that they recycled the overall structure of the other article. Plausibly, they denied any allegations of plagiarism.
Being inexperienced in detecting plagiarism, I am uncertain whether this is any serious violation or academic miscondct.
So, again, I would like to ask:
1. Should my findings suggest any suspicion that one of these articles might have been plagiarized?
2. If the answer is "Yes", what should I do?
Any kind advice would be much appreciated.
If I have mistaken, I would like to send my apologies to the authors of both articles and those who help enlighten my mind.
Attached to this discussion is an excel file detailing the similarities that I found.
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I want to ask the question regarding the approach to solving the combinatorial optimization problem (COP). Based on my reading, some of the researchers proposed an Exact approach to solve the COP rather than a Heuristic approach. As known, the exact approach may not suitable to solve real-world COP on a large scale due to the computational time to provide the solution. But the Heuristic approach can provide the solution with the relational computational time near to the optimal solution. My question why the Exact approach still becomes the choice for some of the researchers rather than directly using the Heuristic approach? Thank you.
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The exact methods are more suitable when the complexity of the task allow solving it and the heuristic methos in other case by the reason there are not sured approaches for the estimation of the possible error while solving. The problem consists in defining the imposibility of solving by exact methiod, by the reason of the possibiity of the tasks decompossition. By this, it is needed to deep in the combinatorial theory before deciding that the faced problem is not possible to solve by exact methods
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why we use meta heuristic optimization algorithms to solve multi-level image segmentation, however the machine learning and deep learning can perform?
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I'm looking for a heuristic algorithm to solve facility location problem
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There is not just one location problem - there are rather many special cases. If I were you I would start by simply use Google, and check the models that you can found, such that you have a variety to look at - and perhaps one of them is what you need!
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I am looking to develop an overview/survey of specific experimental techniques and papers in which exploration is defined, measured, and analyzed as part of heuristic search (preferably for continuous domains).
Suggestions and references very much appreciated.
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I just wanted to say that I'm quite partial to the recent paper by Marjan Mernik
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Hello
that is, so that I would like to implement a network that consists of 16 nodes (see the figure below) after I have implemented it, I want to combine the network with a heuristic and it becomes the nearest neighbor heuristic. Given that I have the costs between the nodes. The vehicle in the middle should travel and represents the shortest route.
How can I proceed? Can anyone help me how I can implement a network and combine the heuristics in it using matlab or java.
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I would like to implement a network that consists of few nodes (see the figure below) after I have implemented it, I want to combine the network with a heuristic and it becomes the nearest neighbor heuristic. Given that I have the costs between the nodes. The vehicle in the middle should travel and represents the shortest route.
How can i code it ? Need a code for implement a network and combine the heuristics in, using matlab.
I approximately found a code below that matches my problem (see figur ) but the code counts the nearest neighbor directly but I want to divide the task myself and then it will count the nearest neighbor
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Based on the available code, make the following modification:
Define a weighted distance as the function of the costs. The rest of the code is suitable for your objective.
But, about dividing tasks, it is a little ambiguous. Please explain more.
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Multi-Objective Particle Swarm Optimization (MOPSO) method is known as a heuristic optimization technique thatThe multi-Objective does not guarantee to reach global optimality. Why the algorithm is convenient for most of the targeted applications? Are they other potential solution approaches? Is it conceivable to use standard optimization solvers like, e.g., CPLEX. The multi-Objective
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It depends on the dimension size and multi-modality level of the optimisation problem. If the problem size is small and convex, CPLEX can be a better option.
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need help to find a good heuristic for multi vechile ?
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if you want exact methods, you can use branch and bound or column generation algorithms.
if you want heuristics, you can use Saving heuristic, sweep heuristic, and others
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Need heuristic for assignment problem. Use it in order to allocation tasks to 2 or more vehicle. So it can work on the same network. The heuristic should be easy to implement for exempel not GA.
NOTE the allocation of the task can be for exmpel vehicle 1 pick a goods from nod A to B and vehicle 2 pick from C to D.
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I can't understand the problem, either. At first, it sounded like a vehicle routing problem. But then, when you mention shelves, goods placed on them and a corresponding coordinate system, it sounds like optimizing warehouse operations. Are you trying to schedule the movements of forklifts in a warehouse? Optimizing an automated material handling system? You need to provide more information so that the problem is understood by everyone here.
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Some people are not impressed by the development of intuitive near-optimal closed-form solutions to some business problems because the exact optimal solutions can be obtained using a spreadsheet solver. The objective functions do not lead to exact closed-form optimal solutions. The approximate closed-form optimal solutions are very intuitive from a business perspective. My argument is that Little's Law is used to estimate the average WIP levels when you know the average throughput rate and the average cycle time, and it is applied in many different contexts. Of course, you can model all of the complexities of the shop floor and make this calculation more accurate. Aren't we better off if we can come up with some simple and intuitive equations that fit many business scenarios? Solving to exact optimum is in fact not reliable either, because the parameters are not quite precise in the first place.
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This question relates to my recently posted question: What are the best proofs (derivations) of Stefan’s Law?
Stefan’s Law is E is proportional to T^4.
The standard derivation includes use of the concepts of entropy and temperature, and use of calculus.
Suppose we consider counting numbers and, in geometry, triangles, as level 1 concepts, simple and in a sense fundamental. Entropy and temperature are concepts built up from simpler ideas which historically took time to develop. Clausius’s derivation of entropy is itself complex.
The derivation of entropy in Clausius’s text, The Mechanical Theory of Heat (1867) is in the Fourth Memoir which begins at page 111 and concludes at page 135.
Why does the power relationship E proportional to T^4 need to use the concept of entropy, let alone other level 3 concepts, which takes Clausius 24 pages to develop in his aforementioned text book?
Does this reasoning validly suggest that the standard derivation of Stefan’s Law, as in Planck’s text The Theory of Heat Radiation (Masius translation) is not a minimally complex derivation?
In principle, is the standard derivation too complicated?
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Its just not. Heat is one of the finest areas of physics, so nothing is really outworldly, just find a book suitable for you to study.
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I am attempting to design a membrane separation unit to separate a gas feed of approximately 94.1 mol% of hydrogen but I am having trouble finding performance equations/sizing parameters and heuristics which could be used to do so. Can anybody recommend any textooks or reports to help with this? If it helps the stream also contains carbon monoxide and dioxide, nitrogen and methane.
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if i understand you in the right direction, you don't want to seperate hydrogen, you want to seperate the rest out of a hydrogen stream. So you know what the rest is ? that would help to answer your question.
Where is the stream from ?
beste regards
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Hi,
I just want to make sure that I understand the mechanics of the NSGA-II (the non-dominating sorting genetic algorithm) for a multiobjective optimization problem, since the resources I have (I am not satisfied with, I would be grateful if anyone can recommend me a good paper or source to read more about the NSGA-II)
Here is what I got so far:
1- We start with a random population lets call P_0 of N individuals
2- Generate an off-spring population Q_0 of size N from P_0 by using binary tournament selection, crossover and mutation)
3- Let R_0=P_0 U Q_0
While (itr<= maxitr) do,
5- Identify the non-dominating fronts in R_0, (F_1,..F_j)
6- create P_1 (of size N) as follows:
for i=1:j
if |P_1| + |F_i| <= N
set P_1=P_1 U F_i
else,
add the least crowded N - |P_1| solutions from F_i to P_1
end
end
7- set P_1=P_0;
8- generate an off-spring Q_0 from P_0 and set R_0=Q_0 U P_0
9- itr=itr+1;
end(do)
My question (assuming the previous algorithm is correct, how do I generate Q_0 from P_0 in step 8?
Do I choose randomly any 2 solutions from P_0 and mate them or do I choose according to or is it better to select the parents according to some condition like those who have the highest rank should mate?
Also, if you can leave me some well-written papers on NSGA-II I would be grateful.
Thanks
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I am a bit muddled up with respect to tournament selection..
Should the population set size be the same as the size of the offspring?
If that is the case, why do we need a tournament selection strategy to form a mating pool- we might as well as directly use the entire population as a whole?
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Hello, I am hoping to use Heuristic Inquiry to explore lived (living) experiences of educators and online networks and would love to connect with other Researchers who have used this methodology and might be able to share some hints and tips about things you have learnt along the way ? A lot of the research I have been reading stresses that it is really difficult and not for everyone so I am hoping to find people who would recommend it, and the transformative journey that they have been part of ?
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Good question, Debbie - many doc scholars pursue autoethnography but less for HI. I sent you a private message as well and let me know if I can offer anything further on the distinctions for using heuristic inquiry! Bravo to you ~
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I am looking for advice concerning a (supposedly) known practical issue : article overloads. While doing my PhD I was convinced that everything who went through publication was worth reading and understanding. My opinion as evolved since then for very practical consideration : lack of time to read biblio and absolute necessity to "pre-screen" something before deciding if it's worth reading or not.
Concerning scientific paper, the prescreening can be tricky. Since the format is very standardized as well as the wording (nothings sounds more like a paper than a paper), I often end up reading half a dozen page on a paper, annotates parts, spend time... before deciding I shouldn't spend time on it.
Do you have some "tricks" to share in order to lower that waste of time? While these "tricks" might be completely non-scientific of course, I still would enjoy them
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If you are reading the prior work just for your literature review, you don't have to meticulously read them as if you are reviewing them as a referee. However, if you identify some errors or shortcomings, take a note of them, perhaps you might end up with another paper idea from them. You just have to identify what is different compared to your paper and how your paper is an improvement or a different but important paper looking from another aspect, etc. If it is highly related to your paper, and you need to be very specific to convince the readers that your contribution is significant, then you should read it very carefully. If you need to get a general idea about the area, for a potential research, then you will need to read the most important (highly cited) and early work on that subject very carefully. You also need to read the most recent work, to be up-to-date on the subject. Your reading of the earliest, the most influential and the latest papers on the subject will lead to more papers to read, to guide your literature review and to improve your understanding of the state of the art in the area.
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Heuristics reduces the computation time of creating clusters from a set of data points. But, selecting the right heuristic algorithm with fine-tuning is a challenging task. I want to know what are suitable meta-heuristic algorithms available for good performance in cluster building.
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Dear Faisal,
This definitely depends on the clustering model that you are willing to solve.
If you mean the minimum-sum-of-squares clustering model (the one for which K-means is a natural local minimizer), you can check the following article for an exact method based on column generation (quite efficient for small and medium problems):
and the following article plus related references regarding modern metaheuristics for larger problems:
Good luck with your research!
--Thibaut
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Hi, I am working on a research paper in which I want to compare the performance of several (meta)heuristics (including GA) in solving a certain problem. I have run each algorithm several times and found out that my GA is not able to find the good solution that other (meta)heuristics find in a short time. It converges to a solution which I know is not the best (because other algorithms converge to a way better solution. I have increased the mutation rate to 0.2 in order to avoid getting trapped in a local optima and my crossover rate is 0.9.
I want to have an acceptable comparison/evaluation of the performance of these algorithms, So
my question is: Is there a problem with my GA or can I simply report the GA solution and explain that it performs poorly?
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One possible explanation is that standard GAs do not include local search as part of their implementation. Metaheuristics such as Iterated Local Search incorporate local search and that makes them very effective. One solution is to hybridize the GA with local search, which is sometimes called Memetic Algorithms.
Another explanation is that sometimes Crossover operators are not very effective. So a GA with a very effective crossover can have good performance, but another GA with a mediocre crossover operator can have poor performance.
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I have some optimal solutions of a discret space and I want to apply an heuristic search using those solutions as attractors. I started using distances as cost functions but I don't know if it's a good approach.
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Antonio Bolufé-Röhler Thanks for your answer.
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I am programming a scheduling system using simulated annealing and I want to know if this heuristic is suitable?
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In general, a heuristic might be suitable when an exact algorithm cannot easily solve a problem. Heuristics have increased in popularity because the problems we want to solve become more complex as time passes. Which one to use would depend on the type of problem being solved, as Tsung-Che Chiang suggests.
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In recent years, many new heuristic algorithms are proposed in the community. However, it seems that they are already following a similar concept and they have similar benefits and drawbacks. Also, for large scale problems, with higher computational cost (real-world problems), it would be inefficient to use an evolutionary algorithm. These algorithms present different designs in single runs. So they look to be unreliable. Besides, heuristics have no mathematical background.
I think that the hybridization of mathematical algorithms and heuristics will help to handle real-world problems. They may be effective in cases in which the analytical gradient is unavailable and the finite difference is the only way to take the gradients (the gradient information may contain noise due to simulation error). So we can benefit from gradient information, while having a global search in the design domain.
There are some hybrid papers in the state-of-the-art. However, some people think that hybridization is the loss of the benefits of both methods. What do you think? Can it be beneficial? Should we improve heuristics with mathematics?
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I am surprised that a known scholar with a long experience in the transportation domain maintains such a hard stance on heuristic search. Obviously, we live in a world where extreme opinions are those which are the most echoed. Truth is, assuming that all practical optimization problems can be solved to optimality (or with approximation guarantees) is essentially wishful thinking. Given this state of art, better integration of exact and heuristic algorithms can largely benefit the research community. At the risk of repeating myself, here are some important remarks to consider:
• CPLEX and Gurobi (the current state of the art solvers for mixed integer programming optimization) rely on an army of internal heuristics for cut selection, branching, diving, polishing, etc... Without these heuristic components, optimal solutions could not be found for many problems of interest. CPLEX has even recently made a new release permitting a stronger heuristic emphasis (https://community.ibm.com/community/user/datascience/blogs/xavier-nodet1/2020/11/23/better-solutions-earlier-with-cplex-201). MIP solvers also heavily depend on the availability of good (heuristic) initial solutions to perform well. For many problems, cut separation is also done with heuristics. In the vehicle routing domain, we have a saying: heuristics are the methods that find the solutions, exact methods are those that finally permit to confirm that the heuristics were right (sometimes many decades later, and only for relatively small problems with a few hundred nodes despite over 60 years of research on mathematical models)...
• The machine learning domain is quickly taking over many applications that were previously done with optimization. Among the most popular methods, deep learning applies a form of stochastic gradient descent and does not guarantee convergence to optimal parameters. Neural networks currently face the same scrutiny and issues as the heuristic community, but progress in this area has still brought many notable breakthroughs. Decision-tree construction and random forests are also largely based on greedy algorithms, same for K-means (local improvement method) and many other popular learning algorithms.
• Even parameter tuning by the way is heuristic... I'm sorry to say that, but most design choices, even in the scientific domain, are heuristic and only qualify as good options through experimentation.
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I'm interested in the phenomenological method/paradigm, but have so far not found any papers or projects concerning their utility in interventions. Are heuristics such as Moustakas simply not applicable in the therapeutic setting or am I merely too inexperienced to find the right sources?
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Michael, trouble is do any therapists and psychiatrists read it?
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Instead of manual tuning of algorithm's parameters, it is recommended to utilize automatic algorithm configuration software. Mostly beacuse it was shown that they increase manyfold the algorithm's perfomance. However, there are some differences among the proposed configuration software and beside those listed in (Eiben, Smit, 2011) it is important to gather experiances from the researchers. I would like to hear how does one decide on the stopping criteria, or values for parameters, for heuristic steps within the stochastic algorithm... there are so many questions.
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As you mentioned, parameter tuning studies for a metaheuristic is quite important. Researchers should determine proper control parameters for their optimization problem to increase the success of the algorithm. However, many researchers uses algorithm parameters suggested by their developers as this is can be a time consuming task via a trial and error approach. Also, I agree that self-adaptive versions of these algorithms can increase both effectiveness and performance compared to their original versions. However, they can require definition of extra parameters as well in the algorithm. In my cases, I prefer to use original versions of the algorithms via a parameter tuning study. Besides, I use two termination criteria including a predefined maksimun generation number and a tolerans value. If the algorithm provides a misfit value less than the tolerans, it stops before the reaching maksimum number of generation. Sometimes I take into account a number of successive generations. For instance, if the solution do not improve during the last 30 generations, I stop the algorithm. This provides relatively decrease the high computation cost due to much execution of the forward equation. This is the biggest drawback of the global optimization compared to derivative-based approaches considering high-dimensional optimization problems.
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I just heard of the terminology "black box optimization". I am a little confused about what does it mean! as the name suggests and as I learned is that you are trying to design an algorithm that optimizes an objective function but the algorithm doesn't know (or allowed to use) any prior knowledge about the structure of the function?
So what is not allowed in blackbox optimization:
Using any information derived from the analytical expression to adjust the algorithm?
(So if I know that a given function is multimodal and I know it's global minimum beforehand and I'm using a heuristic algorithm so I'm not allowed to adjust the parameters in a certain way that I know it works for this class of functions. Is this correct?
If this is true, then what is the point of black box optimization?
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When there is a function that we cannot access but we can only observe its outputs based on some given inputs, it is called a black-box function.
On the other hand, black-box optimization (BBO) deals with optimizing these functions. Tuning of large neural networks is considered as an example of these functions.
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The choice of something to ruin can be an implicit choice as to what should be preserved.  A heuristic for preservation can thus lead to a heuristic for ruin.  I've had what I think is a very interesting result for what to preserve (common solution components) in the context of genetic crossover operators that use constructive (as opposed to iterative) heuristics.  I tried to share it with the Ruin and Recreate community with no success.
I guess my real question is -- How should I Ruin and Recreate this research to make it more relevant to Ruin and Recreate researchers?
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In general, my impression of a ruin and recreate process would be to change assignment(s) to decision variables (randomly or otherwise) in a feasible solution, effectively ruining it (in value) and perhaps making the solution infeasible. Then, some sort of repair operator(s) are applied to place the solution back in the feasible region of the solution space.
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Any decision-making problem when precisely formulated within the framework of mathematics is posed as an optimization problem. There are so many ways, in fact, I think infinitely many ways one can partition the set of all possible optimization problems into classes of problems.
1. I often hear people label meta-heuristic and heuristic algorithms as general algorithms (I understand what they mean) but I'm thinking about some things, can we apply these algorithms to any arbitrary optimization problems from any class or more precisely can we adjust/re-model any optimization problem in a way that permits us to attack those problems by the algorithms in question?
2. Then I thought well if we assumed that the answer to 1 is yes then by extending the argument I think also we can re-formulate any given problem to be attacked by any algorithm we desire (of-course with a cost) then it is just a useless tautology.
I'm looking foe different insights :)
Thanks.
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The change propagation models may give a great idea
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Dear fellow researchers,
I need a two to three non indian reviewers for the research area of Scheduling-optimization-meta heuristics-operation research. all the journals are asking for other nationality reviewers, since i dont know anyone can somebody please volunteeer to be my reviewer?
thanks in advance.
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Soumaya Ait Bouziaren hello. please check your inbox.
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I have programmed several heuristic algorithms in my Phd thesis.
The last algorithm gave me very good results as an objective function and even in runtime compared to other algorithms done before. Is there a formula to calculate the gain and how to interpret it? thanks in advaced
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@ Mohamed Azab : first of all thank you, i mean by the "gain" what is the added value and the benefit, how to decide that the algorithm x is better than algorithm y, on what i can rely to know?
Mohamed EL-Shimy : thank you do much. A "Gap" formula is used to know the Gap between two algorithms, can i use for example the cross over formula to know the benefit comparing algo x with ago y ?
@ Richard Epenoy , @ Juan Manuel Izar : i agree with you but i need some more details depending on your exeperience, thank you very much for both of you.
@ Tatjana Jakšić Krüger : thanks so much i totally agree with you.
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hi
I have designed a meta-heuristic algorithm and I used Taguchi Method on a small example should I repeat these experiments for each problem or that's enough because for my small example I can only create 38 neighbor solutions but for my bigger problem I can make 77 neighbor solutions and I think it's important that how many neighbor solutions I can Make & how many neighbor solutions I want to create?
PS: the only difference between the two problems is their size.
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what is difference between heuristic and meta-heuristic algorithms. How can we say a algorithm whether it is heuristic or meta-heuristic algorithm? Thank you in advance.
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Section two of he following article has a very detailed table about this crucial subject:
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Is there really a significant difference between the performance of the different meta-heuristics other than "ϵ"?!!! I mean, at the moment we have many different meta-heuristics and the set expands. Every while you hear about a new meta-heuristic that outperforms the other methods, on a specific problem instance, with ϵ. Most of these algorithms share the same idea: randomness with memory or selection or name it to learn from previous steps. You see in MIC, CEC, SigEvo many repetitions on new meta-heuristiics. does it make sense to stuck here? now the same repeats with hyper-heuristics and .....   
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Apart from the foregoing mentioned discussion, all metaheuristic optimization approaches are alike on average in terms of their performance. The extensive research studies in this field show that an algorithm may be the topmost choice for some norms of problems, but at the same, it may become to be the inferior selection for other types of problems. On the other hand, since most real-world optimization problems have different needs and requirements that vary from industry to industry, there is no universal algorithm or approach that can be applied to every circumstance, and, therefore, it becomes a challenge to pick up the right algorithm that sufficiently suits these essentials.
A discussion of this issue is at section two of the following reference:
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I am preparing a comparison between a couple of metaheuristics, but I would like to hear some points of view on how to measure an algorithm's efficiency. I have thought of using some standard test functions and comparing the convergence time and the value of the evaluated objective function. However, any comments are welcome, and appreciated.
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The 7th section, namely "Results, Data Analysis, and Comparison", of the following current-state-of-the-art research paper have a sufficient answer for this question:
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How different by giving its global optimum?
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Dear All
The following state-of-the-art paper has a detailed explanation of this Question:
Furthermore, it contains a two-page table about the differences between them.
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Can heuristic or meta-heuristic fuzzy clustering algorithms help me? Any suggestions generally? I want to create learner’s profiles based on computational intelligence methods. The number of the groups (profiles) is unknown.
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As you may be knowing that there are different mathematical tools and techniques which we can combine or hybridize with heuristic techniques to solve their entrapment in local minima and convergence issues. I know two techniques namely Chaos theory and Levy distribution as I have used them for increasing convergence speed of Gravitational Search Algorithm (GSA). So, my question is: can you name and briefly explain other mathematical techniques which we can combine with optimization algorithms in order to make them fit for solving complex real world problems.
Thank you.
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Check out the proceedings of the "Matheuristics" conferences. The conference is devoted to the combination of heuristic methods and traditional mathematical programming methods (linear, nonlinear, integer programming, etc.)
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Please i need recommnedation on texts or literature that can improve my knowledge and skills on tuning of control systems ranging from sliding mode, LQR/LQG and others. I alwys have problem at this stage after rigor of modeling.
Most of control design problem involves tuning heuristically. In my opinion, this is randomness that doesnt have strategies. Even PID control with popular Ziegler Nichols still involve randomness!
there should be a way to know the range of tuning.
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I am trying to understand whether the PERMA theory is a good theory. Can the theory be generalized? Can the theory produce solutions to real life problems?
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I agree with Amar’s point above. In my experience, the theoretical and even tested value of any model of inquiry like PERMA or its Japanese cousin Ikigai, or for that matter models of behavior, e.g., competencies, values, team norms, lies less in the brilliance of its design and more in the integrity and diligence of its application.
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All in the question
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Typically not even a near-optimum, Rasel, in a large-scale case - which is really the only case that is of interest these days.
The meta stuff is clearly over-represented at RG - much too much is discussed about it, and it is un-deserved, as there is very little theory behind it, and the practice is sub-par, too - at least in almost every paper I have been able to see the battle among mathematical optimisation and meta stuff.
Mathematical optimisation is the way to go, almost always, and mathematical optimisation is still under development. I just don't fathom why there is so much written about meta stuff, when mathematical optimisation offers so much more! I know that part of it is due to the fact that knowledge in mathematical optimisation is very sparse and primitive, but there is every chance to learn it. Buy a book on mathematical optimisation, check out all the tutorials that you can find, and make a comparison. Come back in a Month and let me know what you have learnt. :-)
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Hi,
I've recently read that the use of random keys in RKGA (Encoding phase) is useful for problems that require permutations of the integers and for which traditional one- or two-point crossover presents feasibility problems.
For example: Consider a 5-node TSP instance. Traditional GA encodings of TSP solutions consist of a stream of integers representing the order in which nodes are to be visited by the tour.1 But one-point crossover, for example, may result in children with some nodes visited more than once and others not visited at all.
My question is: if we don’t have a feasibility problems and our solutions are all feasible solutions so in this case is it correct to apply RKGA?
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Hi.
Random-key-based (RK) approaches are used when a swarm or evolutionary algorithm encodes its individuals with real-valued vectors, and it is applied to solve some permutation problem (solutions are sequences of integers and RK maps a real-valued vector in one sequence of integers). If the algorithm uses an integer-based individual, RK is not used, but you should guaranty that disturbing operators (crossover, mutation, or other) generate only feasible solutions.
Commonly, 1-point crossover (and other crossover operators) create infeasible integer-based offspring, and a repair mechanism is needed.
Please check the paper of Puljić and Manger: Comparison of eight evolutionary crossover operators for the vehicle routing problem ( ) for a detailed description of genetic operators used to generates feasible integer-based offsprings.
Furthermore, RK is also used when integer-based vectors are used as individuals, but the disturbing operators (the mutation operator employed by the differential evolution algorithm, by example) creates real-based offsprings, and these new individuals should be repaired.
In my opinion, if your algorithm uses integer-based individuals, and your crossover and mutation operators generates only feasible solutions, neither RK nor any repair mechanism should be applied.
Best regards!
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According to French 2001, Decision models can be used in the descriptive, normative, or prescriptive analysis. While there is a lot of research performed on normative models (neoclassical) and descriptive (behavioral economics mostly). when researching the various database I can see that prescriptive literature is really thin. I am therefore asking the community if there is any peer-reviewed prescriptive model article for real estate investment to recommend?
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No much idea
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Hello scientists,
I'm looking for a detailed comparison between Routing Machines (how i call them).
Somewhat like a state-of-the-art, survey or tabular comparisons between different alternatives for offline point to point routing frameworks (like Graphhopper or OpenStreetMapRoutingMachine)
Could you point me to some documents where I can research the following information:
  • which map material is the framework working on (not neccessarily OpenStreetMap Data)
  • is the framework able to consider traffic data provided by me
  • is it possible to calculate the fastest route by time
  • does the framework provide the functionallity to calculate a route with many stops
  • if yes, how many
  • which routing heuristic is used
  • does the routing heuristic consider given time-windows for stops
  • and how long does it take in average to route several scenarios
  • what information does the frameworks routing functions provide as output (step by step instructions, polyline, ...)
  • do i have to pay for the framework
  • if yes, how much
Thank you very much,
Richard
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Nice Dear Mohamed-Mourad Lafifi
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I'm working on a helmet-impact test in which when I'm doing front impact a warning is coming out as warpage angle and violation of heuristic criterion.
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Shreya Srivastava Hi, does the previous answer solve the problem? because I find this problem too.
Thanks
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Dear Long Nguyen Cong,
Can you able to generate Levy random number in the interval [a, b] using Levy distribution.
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Dear all,
I have developed a mathematical model ( convex mixed-integer nonlinear programming) in which there is only one nonlinear constraint (which is not quadratic). What is the best method in order to tackle this problem? Thanks
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There is a very useful survey on convex MINLP by D'Ambrosio and Lodi (2013). Also Ignacio Grossman has written some good surveys.
I think Outer Approximation could work well in your case.
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Hell, everyone. I am a student of electrical engineering and my research field is related to the optimization of a power system.
I know that the algorithm that we should choose depends on our problem but there are lots of heuristics, metaheuristic algorithms available to choose from. It will also take some time to understand a specific algorithm and after that maybe we came to know that the chosen algorithm was not the best for my problem. So as per my problem how can I choose the best algorithm?
Is there any simple solution available that can save my time as well?
Thank you for your precious time.
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As most people have indicated the best solution depends on the 'surface' you are optimising and the number of dimensions. If you have a large number of dimensions and a smooth surface then traditional methods that use derivatives (or approximations to derivatives) work well such as the Quasi-Newton Method. If there are a small number of dimensions and the surface is fairly sensible but noisy then the Nelder and Mead Simplex works well. For higher dimensions with noise but still farily sensible (hill like) then simulated annealing works. The surfaces which are discontinuous and mis-leading are best addressed with the more modern heuristic techniques such as evolutionary algorithms. If you are trying to find a pareto-surface then use a multi-objective genetic algorithm. So the key things are how many dimensions, is the surface reasonably smooth (reliable derivatives), do you want a pareto surface or can you run multiple single criterion optimisations. The other questions is, do you need to know the optimum or do you just want a very good result. There are often good algorithms for approximations to the best result, for example using a simplified objective function which can be found much faster to get a good rough solution which may be the starting point for a high fidelity solution. Sorry if this indicates it is complex, it really does depend on the solution space. Do not forget traditonal mathematical methods used in Operational Research as well. Good Luck!
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job shop scheduling problem using dynamic programming
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Make sure to record both CPU time and memory requirements - if one uses too small instances the conclusions will be wrong.
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I am a theoretical physicist and I sometimes use Mathematica to algebraically manipulate large equations. I though use it heuristically and I know a lot of researchers use Mathematica for symbolic computation.
What are the best ways to learn it.
Are there any books or any online course to understand it
What are good practices.
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I have used Maple for years and absolutely love it! I've also used Mathematica and the Wolfram web site is the boss! I can really appreciate these resources because I learned higher math in the stone age, when we had to do everything by hand. I derived equations that went on for a dozen pages. One tiny mistake along the way ruined the outcome. At least I had a pencil and didn't have to chisel equations into stone. Be very thankful for the technology but don't neglect the theory. Knowing why and how is as important as what (getting an answer)!
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I am working on an assignment problem to agent with high matching .
Consider KK agents A1,…AKA1,…AK and NN tasks T1,…TNT1,…TN. Each task has a certain time t(Ti)t(Ti) to be completed and each agent has a matching (or affinity) value associated with each task MAj(Ti),∀i,jMAj(Ti),∀i,j. The goal is to assign agents to tasks, such that the matching value is maximized and the overall time to complete the tasks is minimized. Moreover, an agent can be assigned to multiple tasks. However, an agent cannot start a new task before finishing the previous one.
How can I solve this problem? Can I solve it with multi objective A*? What would be an admissible heuristic function and how to calculate heuristic h(n) function ?
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sure sir i will keep post to you@ Nolberto Munier
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Do you think it is neccery to have software that contains meta heuristic algortms like GA,SA,...
in a package that calculates different modified problems ?
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not necessary but it would be helpful for who dont have enouqh information about programming
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I am working on an Assigning problem to expert or agent
How can I solve this problem? Can I solve it with multi objective A*? What would be an admissible heuristic function and how to calculate heuristic h(n) function ?how can i design multi objective A* algorithm for this problem please help me.
Consider KK agents A1,…AKA1,…AK and NN tasks T1,…TNT1,…TN. Each task has a certain time t(Ti)t(Ti) to be completed and each agent has a matching (or affinity) value associated with each task MAj(Ti),∀i,jMAj(Ti),∀i,j. The goal is to assign agents to tasks, such that the matching value is maximized and the overall time to complete the tasks is minimized. Moreover, an agent can be assigned to multiple tasks. However, an agent cannot start a new task before finishing the previous one.
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If you fix the time limit, the problem reduces to a special case of the Generalized Assignment Problem (GAP). See here:
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Recently, I have just started learning basic mineralogy and I found that it is quite difficult to master. It would help me a lot if you can share some tips on this field of study so that I can easily identify and describe accurately the minerals that is being observed using plane polarised light and cross polarised light.
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Dear Mr. Hanafi,
unless collecting rocks and minerals was one of the launch pads to study mineralogy creating a reference collection of both rock and mineral specimens is recommended. Do no start with the most exotic minerals and rocks but try find common rock types and minerals but different in their outward appearance.
In combination with this practical approach which helps you improve your visual inspection and the use of the classical tools from hardness to crystal morphology read textbooks where simplex chemical systems relevant for litho genesis and mineral formation are discussed.
Get acquainted with the chemical systems and the periodical chart of elements.
The best way to enter classical crystallography is in combination with optical mineralogy. It helps you train your imagination which is the key to successfully master the petrography microscope.
During this incipient stage you will realized where your interests are located in genetic mineralogy, petrology, crystallography or economic geology, applied geochemistry with material sciences which can be an outlet into one of the neighboring disciplines. Be very open-minded and try and get information of adjacent disciplines as much as possible to find your way. If you feel that space and time are more attractive than compositional changes and varying physical-chemical conditions you need not give up mineralogy as you decide to pass over into geology. I did it the other way round without cutting my geological roots.
There is more than one way to head for Rome.
I wish you much success
H.G.Dill
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What is the best/ next step if you suspect the evolutionary algorithm recombines and mutates in an inferior way, for your nonvonvex nonlinear optimization problem?
Write your own heuristic?
How do you implement it?
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Hi, instead of modifying the mutation rule it would be better in a first step to generate a larger initial population. Indeed, the new mutation rule will be adapted to a particular problem only.
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In psycholinguistic norming studies 15-20 raters per word per scale are somehow rule of the thumb. However, I cannot find the psychometric explanation or justification for this, although. Does anyone have the reference considering this question?
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Hi, Milica. Thee is a huge amount of literature and many tools for sample size and power determination. Google "number of cases needed to estimate a parameter." My first hit is a good one: http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_Power/BS704_Power_print.html
The rule of thumb of 10 cases per parameter is still a pretty good one though, I think. Here is a link that mentions it: https://www.statisticssolutions.com/sample-size-formula/
Incidentally, 15-20 raters "per word per scale" would seem to require a very large number of raters ... and a complicated inter-rater reliability analysis, especially if each rater rated multiple words and you needed to deal with with-in rater variability as well. There are lots of human internet rater services. Maybe you are using one. Good ones probably use advanced analytics to pool rater results, allowing for rater agreemeet variability and other factors.
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How to ensure that my ga code is good enough?
I guess I should test it on various test functions that are different in structure, that's one obvious thing but, what about the number of chromosomes one start with?
Is it a problem dependant? same question on the number of runs.
I would be very grateful if you could send me also some helpful papers on that issue.
Thanks!
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There are many papers that devoted to GA in different test functions. So, the easiest way to define your starting parametres in certain test function is to based on that investigations and then you will see the effectivness of your modifications (and parametres values) compared to existing ones.
What about real problems - your guessing is absolutely right, cause every problem has his own solutions space and it's require individual approach for modifications and parameters setting up.
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Dear Experts,
Greetings!
Looking for your kind opinion and Ref regarding the following CB-SEM issues
Being a new user of AMOS, few of us are facing problem regarding a few specific questions...
Q1: In the model, if the latent variable has 2/3 sub-constructs and each of those sub-constructs have 3,4 and 2 items respectively then is it correct? ( As in many articles its mentioned the rule of thumb is to have minimum 3 items).
Q2: If any latent variable has 2 sub-constructs and during respecified SEM, one of the sub- construct's factor loading comes .50 should we keep that construct? If we need to take out then should we need to drop the variable?
Q3: During the respecified SEM model, if GFI, TLI, CFI, RMSEA met the threshold but p-value shows .000 then will we call that model as the good fit model or we need to make sure p is .05 and above ( Is Must).
Q4: For continuous moderator ( e.g. Work-Life Balance or Locus of Control), in case of latent construct do we need to have 2 models named constrained and unconstrained (Bryne, 2004). ( If this is the only process in AMOS can we have the steps)
Though its a quite long text but this gonna make analysis easy for new AMOS user, including myself😊
Any expert opinion and ref highly appreciated.
Thanks,
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To optimize a production system by planning ~1000 timesteps ahead I try to solve an optimization problem with around 20000 dimensions containing binary and continuous variables and several complex constraints.
I know the provided information is little, but can someone give a hint which approach would be suitable for such big problems? Would you recommend some metaheuristic or a commercial solver?
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One strategy is to define variables as time intervals - if the time interval is used for something, then a variable is set to one, and you then pay the fee - or whatever - for this use. By starting with long time intervals the production planning problem may be easy to solve, as the number of variables is small. You then use that solution as a starting point for a new problem, in which the time-steps are, say, 1/2 or even 1/10th of the previous time interval; your previous solution is feasible in the new problems, which will help the solver to quickly find an optimum to the problem. If you re-iterate like this until you have as small a time interval that you want, your optimum will be the solution of the last problem. (This is a very simple way of performing a discretization procedure.)
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Within my Grounded Theory study I want to analyse attitudes and opinions of a group of people towards a specific topic. Therefore, I would like to adapt the coding paradigm of Strauss & Corbin to this research goal. I read in literature that this is not only possible but also recommendable but I am just not sure how to do it. Has anyone experience in doing so?
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You might also look at what Glaser calls "coding families," and among them check out the "six c's" -- which are close to Strauss and Corbin's original proposal.
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Various meta heuristic techniques are: PSO, ACO, GA etc. I want to know that which one is best to apply in the area of routing optimization in VANET.
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Referring to the No Free Lunch (NFL) Theorem, "any two optimization algorithms are equivalent when their performance is averaged across all possible problems". Therefore, no one can claim that Algorithm X, which is a general meta-heuristic one, is the best for optimizing a given problem. One should adapt and customize it to exploit its advantages as much as possible.
Since Ant Colony Optimization (ACO) Algorithm is intrinsically designed to solve routing problems, I think it can be in your special case, i.e. Vehicular ad-hoc network routing, be more useful if you tune its parameters properly. It should be noted that if your network grow unreasonably, updating ACO's tables takes too much time and applying this algorithm is not logical.
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If ignoring the interpretative significance of feature selection mechanism what makes feature selection worth performing as compared to feature extraction algorithms which is much focused on deriving out the axis where features are independent and more discriminate.Ultimately we are trying to achieve same sets of goal like features to be independent and maximally correlated with output variable or dimension reduction etc
So my question is there any heuristic or some approach, so that we can find out when to go with feature selection or feature extraction based on our application or we just try out both the both of them and find out which works better
If any one can point out some research paper focused on this can help me out.
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Feature extraction is the process of calculating different statistical parameters i.e. rms, average, standard deviation etc. however it is not limited to the statistical features. Basically, the parameters which describes the event better can be called as the feature of the event. On the other hand, feature selection is performed to find out the most appropriate feature before machine learning. It is mainly performed to avoid unnecessary mis-classification and to get the optimum data set for machine-learning.
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We are conducting the research that includes robot learning and machine learning using ANFIS (Adaptive Neuro-fuzy Reference System). I have a question about the recent machine learning study. Comparing with five years ago, for training the machine learning algorithms, there are fewer citations from studies that use heuristic methods such as Particle Swarm Optimization (PSO) or Genetic Algorithm (GA) methods.
Previous studies have shown that these methods are effective, but why do they not much used these days?
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Hi Mindy,
Can you please make your question a bit more clear?
Are you asking why machine learning algorithms, like reinforcement learning, have been getting more popularity than heuristics?
Or, the heuristics aided machine learning methods, such as GA based feature extraction for ANN?
Regards
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I am looking for references to show that GA solutions do not necessarily converge to optimal solution to defend the use of an integer program, exact solution. I want to criticize heuristic and meta-heuristic algorithm, especially GA.
I think a book might be a good reference but not sure which one to use!
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The NFL (not to confuse with the National Football League) is not a wild card to implement whatever stuff that comes to mind. Even in the case where an objective function isn't, say, differentiable, but at least Lipschitz continuous we do have an edge over methods that do not require anything - because they guarantee nothing either. I am referring to the flood of metaheuristics that have no natural stopping criteria beyond # iterations. In contrast Lipschitz continuity provides lower bounds that can be profitably utilized in decomposition methods, along the lines of Branch-and-Bound.
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To be more specific if i have ship speeds of 3m/s , 6m/s , 9 m/s and maximum of 13 m/s , how can i relate these speed values to the propeller rpm so that the ship can be maneuver at those speeds.
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Yes. You have to assume that water is an inelastic medium and that there is no propeller cavitation. With those assumptions, for a given value of pitch, forward motion of the ship is a direct function of propeller RPM.
First we address propeller pitch. To define pitch angle, simplistically because in actual designs the ideal is never possible, a propeller with 0 pitch angle could be described as a disc. It rotates, but creates no thrust at all. A propeller with 90 degree pitch also creates no thrust, and has a difficult time to rotate at all. Because with 90 degree pitch, the blade surfaces are parallel to the shaft.
As you increase pitch angle from 0 degrees, a rotating propeller creates thrust in a direction perpendicular to rotation. Using trig, and looking at the pitch angle Φ at a given radius from the shaft, and knowing that in one full rotation of the propeller, the blade must travel a distance of 2πr, or πd, you can determine that the motion created in one complete rotation of the propeller is:
distance moved in one prop rotation = π * d * tan(Φ)
where the pitch angle Φ applies to blade pitch at a diameter d from the shaft. (Usually the pitch of propeller blades is measured close to the tips of the blades. In real propellers, pitch will increase as the blade approaches the shaft, to prevent cavitation. At every radius point from the shaft, you want the propeller blade to push through the same amount of water.)
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To derive that equation, assume you are dragging a flat plane through water, at an angle from the direction in which it is being dragged. The distance dragged we'll call dd, and the perpendicular distance traveled, as water is pushing the plane sideways, we'll call dp.
tan(Φ) = dp / dd
dp = dd * tan(Φ)
Instead of a plane being dragged a linear distance dd, in a propeller, we have blades being dragged through the water a distance of πd for every rotation of the prop. Replace dd with πd, and you get the equation above, for perpendicular distance moved in one full rotation of the propeller.
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rate = distance / time
Distance moved by ship = π * diam. * tan(Φ) for each rotation of propeller
Distance traveled per minute = π * diam. * tan(Φ) * RPM
Distance traveled per second = π * diam. * tan(Φ) * RPM / 60
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For HPLC, what different mobile phases are best to start with for methods development? What is a good approach to trying different buffers/motile phases for methods development?
I've looked through a few papers, but as I'm new to using HPLC, wanted to know if there is a good 'rule of thumb' of different types of buffer solutions to try first in methods development, and why.
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Try this "free" program that you can play what if in mobile phase method development. It is a good tools that I did not have when I was in school 35 years ago.
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I calculated Lin's (1989) concordance correlation coefficient to get the test-retest reliability (rtt).
I had 78 subjects and the second assessment occurred after 6 weeks.
Are there any references for rules of thumb of what is considered a good/acceptable value. I read values between .7-.9 for Pearson correlations, but did not find values for the concordance correlation coefficient (which is usually a bit lower). Further, rules of thumb are oftentimes stated without references.
I appreciate any suggestions.
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Dear Stephan,
I suggest you to see links and attached file on topic.