Science topic

# 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.

Questions related to Heuristics

Competition @Submit #Relativism ^Nurture *Condition %Graceful (Evolution: Purpose)

How to tune low pass filter parameters through heuristic optimization techniques?

Hi,

What all parameters should one test for when it comes to sound? Is there any heuristics available?

Thanks

I am sharing with you a list of articles that I normally use when supervising my master (and bachelor but mainly master) students. I hope you will find them useful, and I welcome your feedback (i) in order to improve them, (ii) to have new ideas and (iii) anything else that you would like to share.

I tried to keep these articles fairly general, but my perspective come from supervising students in Computer Science/Engineering, Data Science, Information Management and Software Engineering. Therefore, apologies if they may sound alien to your discipline, however, if that is the case please let me know. Over the years they have grown in quantity, and I categorized them in 2 groups:

: articles that clarify various aspects of the development of a (master) thesis. Proposal development, ideas, related work, methodology, writing etc...*How to do a better thesis*: articles that helps a person familiar with scripting programming (basic python for example) understanding the basic of object-oriented programming and how professional tools (like SDK) woks. Again, the specific focus is improving the quality of the code that a master student needs to write. Probably if you are following a hard-core programming master these articles will be fairly simple.*How to become a better programmer*

What follows are the one of the “better thesis” section:

**Attitude mindset and lifestyle**

- Take a moment to reflect to right approach for the challenge ahead. https://francescolelli.info/thesis/the-right-attitude-for-your-thesis-preparing-yourself-for-the-challenge/
- Mens sana in corpore sano: Take care of yourself, in particular do not neglect of your physical health https://francescolelli.info/thesis/simple-rules-for-taking-care-of-yourself-during-before-and-after-your-thesis/

**How to do a good thesis: before you start.**

- Start from considering these tips for improving the quality of your proposal. They will help you in understanding how to think scientifically including if you do not have to write a research proposal. https://francescolelli.info/thesis/how-to-write-a-thesis-proposal-or-a-research-proposal-a-few-tips/
- Check if you are aware of all the players around you thesis and what is their interest https://francescolelli.info/thesis/the-players-around-your-thesis-who-is-going-to-help-you/
- Understand how adopting good scientific practices can improve your grade. https://francescolelli.info/thesis/adopting-good-scientific-practices-increases-your-visibility-and-the-grade-of-your-thesis/

**How to do a good thesis: the openings moves.**

- Ask yourself what you want to do when you will “grow up”. This article will help you understanding how you can take the most from your thesis for your future goals. https://francescolelli.info/thesis/take-the-most-from-your-thesis/
- (Optional) Get a grasp of what kind of mentor I am. It will help you in understanding what I write in these posts and/or if we are compatible in case you are considering pursuing your thesis with me. https://francescolelli.info/thesis/mentor-for-your-thesis/
- Set up the proper communication tools with your supervisor, so that you will have a better quality time with him/her. https://francescolelli.info/thesis/setting-up-the-proper-communication-tools-with-your-thesis-supervisor/

**A**

**Note on Writing:**

- Writing a scientific endeavor has its rules and best practices https://francescolelli.info/tutorial/on-scientific-writing-classic-postmodern-and-self-conscious-style/
- <work in progress>

**How to do a good thesis: literature research and related work**

- Look at these heuristics for understanding if a paper is worth reading or you should move forward to the next one. https://francescolelli.info/thesis/6-heuristics-for-assessing-the-quality-of-a-publication/
- Understand how to select good venues (conferences or journals) where you can search for good publication. https://francescolelli.info/thesis/how-scientific-venues-work-an-heuristic/
- Learn how to read a scientific paper faster and more effectively. https://francescolelli.info/thesis/read-scientific-papers-quickly-and-effectively/
- Master the right features in MS-Word for handling the related work and managing the growing complexity of the task. https://francescolelli.info/thesis/how-to-use-references-in-word-a-few-tips-and-suggestions-for-your-thesis/
- Get more insights about related work, literature review and survey papers. https://francescolelli.info/tutorial/related-work-literature-review-survey-paper-a-collection-of-resources/

**How to do a good thesis: the experimental and scientific part**

- If you feel stuck: get an idea on “how to warm up your research engine” and do your first step. https://francescolelli.info/thesis/warming-up-the-research-engine/
- Get some inspiration from the work of other scientist and learn how to properly categorize the literature review. https://francescolelli.info/thesis/how-to-use-the-literature-review-for-your-research
- Familiarize with sources that can provide Data for your (master or bachelor) thesis. https://francescolelli.info/thesis/where-to-get-data-a-collection-of-resources-for-your-thesis/
- If you plan to write some programming code there are several free resources that can help you. https://francescolelli.info/programming/free-resources-that-will-warm-up-your-programming-environment/
- If you plan to write some programming code, get familiar with these best practices. https://francescolelli.info/how-to-be-a-better-programmer-the-mini-guide/
- If you plan to use a survey for scientific research you may want to consider these tips and suggestions. https://francescolelli.info/thesis/get-the-basics-on-doing-a-survey-for-scientific-research-purposes/
- Do this simple feasibility check if you plan to use an interview approach in your case study research https://francescolelli.info/thesis/should-you-use-a-case-study-for-your-thesis-in-information-management/
- <work in progress>

**How to do a good thesis: the last mile**

- Did you produced the first final draft of the thesis? Here you can find a simple set of rules and a checklist that can help you. https://francescolelli.info/thesis/simple-writing-rules-that-can-improve-the-quality-of-your-thesis/
- Are you close to finishing the thesis? Put your current draft to a (stress) test. https://francescolelli.info/thesis/the-navigation-test-put-your-thesis-to-a-stress-test/

**The End Game**

- Deal with the submission of your thesis and its defense in the proper way https://francescolelli.info/thesis/commencing-the-end-game-last-minute-issues-and-recommendations/
- Understand what is Open Access and how you can make the most of it https://francescolelli.info/thesis/should-you-release-your-thesis-open-access/
- Consider the benefit (and the extra work) of publish your thesis. Is it worth it? https://francescolelli.info/thesis/should-i-publish-my-thesis-the-good-the-bad-the-ugly/
- Now that your thesis has been submitted is about preparing a killer presentation for the defense! https://francescolelli.info/thesis/the-art-and-the-skill-of-speaking-and-making-a-presentation

**The End of the Journey**

- Publish your thesis using the University Library. It will take less then one hour and will ensure some extra visibility to your work. https://francescolelli.info/thesis/publish-your-thesis-in-your-university-library/
- Learn what the future of your thesis could be https://francescolelli.info/thesis/what-will-happen-to-your-thesis-after-your-graduation/

Thanks for taking the time to read such a long discussion! Based on your experience, is there anything missing or that require some improvement? Drop me a line, I will be happy to hear from you

Francesco

Associated matters: How are heuristics rated? What is the realtime impact on sustainability given the thrust towards decisioneering? How can "why" be known transparently while defining the meta-privacy interests of key sources?

Qm is the ultimate realists utilization of the powerful differential equations, because the integer options and necessities of solutions correspond to nature's quanta.

The same can be said for GR whose differential manifolds, an sdvanced concept or hranch in mathematics, have a realistic implementation in nature compatible motional geodesics.

1 century later,so new such feats have been possible, making one to think if the limit of heuristic mathematical supplementation in powerful ways towards realist results in physics in reached.

I am interested in any articles that have heuristics in the title.

I am the author of Programmable Heuristics:

I have been wondering if there may be methods of applying my heuristics to commercial applications like games, or improving the web.

Does anyone have experience with something mildly related, even if it's just using HTML L-frames or imbedded excel sheets?

I would like to see heuristics applied in the real world, but I have seen few examples of that.

My sense is it could be mathematical enough that it could simply organize text or determine outputs fairly easily, as it is designed to be simply organized.

Thanks for your help!

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

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?

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.

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

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.

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?

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?

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?

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?

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?

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.

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.

why we use meta heuristic optimization algorithms to solve multi-level image segmentation, however the machine learning and deep learning can perform?

I'm looking for a heuristic algorithm to solve facility location problem

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.

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.

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****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

**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.

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.

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.

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

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 ?

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?

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

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.

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?

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.

I am programming a scheduling system using simulated annealing and I want to know if this heuristic is suitable?

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?

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?

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.

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?

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?

Conference Paper The GENIE is out! (Who needs fitness to evolve?)

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

*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.***if we assumed that the answer to 1 is yes**I'm looking foe different insights :)

Thanks.

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.

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

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.

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.

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 .....

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.

How different by giving its global optimum?

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.

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.

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.

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?

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?

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?

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

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.

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

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.

job shop scheduling problem using dynamic programming

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.

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 ?

Software for developing heuristics in time-scheduled network

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 ?

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.