Science topic
Genetic Algorithm - Science topic
Genetic Algorithm are solving problems in maths by optimization technique using GA
Questions related to Genetic Algorithm
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Currently, when performing a single and multi-objective optimisation in MATLAB for Genetic Algorithm, I'm setting the upper and lower bounds based on the maximum and minimum process parameter values used in Design of Experiments (DoE) in order to cover the range of values that were used to conduct my experiment, and by extension to obtain my performance measures and regression equations.
Apart from using the upper and lower bounds of these process parameters, is there any other techniques that can be used to set this range based on current studies/research when conducting these types of optimisation techniques?
Thank you in advance.
I am working on solving a system of linear equations with multiple variables. The system is of the form Ax=b, where the matrix A is extremely ill-conditioned. I have been struggling to find a stable solution for this problem. I have tried several numerical approaches, including genetic algorithms and other optimization methods, but I have not been successful in obtaining a reliable solution.
Here are some additional details about the problem:
1. The system is ill-conditioned, leading to significant numerical instability.
2. I know the boundaries of the solution space for x.
3. I also have the real solution for x that can be used for comparison.
I have been working on this problem for 4 months and would greatly appreciate any advice or suggestions on possible methods I could use to solve this problem. Below is the code I have been working on:
import numpy as np
Example of a linear system Ax = b
in attached file
- I calculated the condition number of the matrix using cond_number = np.linalg.cond(A), and the result is 5.956181138617321e+23, which indicates a highly ill-conditioned matrix.
- So far, I have attempted to solve the problem using SVD and genetic algorithms.
- I am trying to simulate the approach described in [this research paper][1]. The simulation involves estimating a line using moments specifically, 15 moments. The challenge arises because the higher-order moments generate extremely large coefficients (on the order of 10^28) in the later equations, while the coefficients of the earlier equations are relatively small (on the order of 10). This disparity complicates the solution of the system of equations.
Any suggestions on how to handle such an ill-conditioned matrix and the wide range of coefficients would be appreciated.
Any other TWO (2) genetic algorithms
Expected results from the Matlab codes for optimization
a. Display Optimal value for the function
b. Display Optimal values for the variables
c. Display graph of optimal values vs number of iteration
d. Display graph of each variable vs number of iteration
Does it depend on application?
Good day, everyone.
I am trying to create an artificial neural network with Matlab and then optimize it with the genetic algorithm. After training the network, I saved it and created the following two script files.
function fitness = fitnessFunction(x, trainedNetwork)
% Check input values
net = trainedNetwork;
if any(x < -1) || any(x > 1)
error('Input values must be between -1 and +1.');
end
try
outputs = net(x'); % use net instead of trainedNetwork
fitness = -sum(outputs); % Negative sign, since the genetic algorithm thinks it is looking for a minimum
catch ME
rethrow(ME);
end
end
%Install trainedNetwork
load('trainedNetwork.mat', 'trainedNetwork'); % Load the file where the trainedNetwork variable is saved
inputSize = 3;
% Set genetic algorithm settings
% Set lower and upper limits (between -1 and +1)
lb = -ones(1, inputSize); % Lower limit
ub = ones(1, inputSize); % Upper limit
options = optimoptions('ga', 'Display', 'iter', 'PopulationSize', 100, 'MaxGenerations', 50);
% Run the genetic algorithm
[x, fval] = ga(@(x) fitnessFunction(x, trainedNetwork), inputSize, [], [], [], [], [], lb, ub, [], options);
% Best solution and fitness value
bestInputs = x;
bestFitness = -fval; % We return it because we have marked it negative
% Calculate outputs corresponding to the best inputs
bestOutputs = trainedNetwork(bestInputs');
disp('Best inputs:');
disp(bestInputs);
disp('The highest outputs corresponding to these inputs:');
disp(bestOutputs);
When I run the second script, I get the following error.
Array indices must be positive integers or logical values.
Error in fitnessFunction (line 8)
outputs = trainedNetwork(x');
I would be very grateful if you can help me.
I use Design Expert to generate regression equations from raw input data. These regression equations are integrated into MATLAB to perform genetic algorithm single and multi-objective optimisation using the MATLAB optimisation tool. I've been looking to utilise ANFIS using the MATLAB fuzzy logic designer to fine tune the raw data, so that the fine tuned input data and output data can be used for the optimisation instead.
However, the exports I get from the ANFIS simulation results are not (in my opinion) very clear regarding how I can use these fine-tuned datasets subsequent to training the model and obtaining the Root mean square errors. How can one determine the fine tuned input and output data after the training is complete? I see that the training model generates ranges for each of my membership functions per input but from that range, it is difficult to ascertain whether it can give me a specific dataset of values for input and output compared to the raw input and output data that was entered.
The steps I have performed thus far:
1. I used 85% (26) of the total data set (30) to train the Sugeno model
2. I used the remaining 15% (4) of the total data set to validate, from which I got tuned predictions of the 4 reference outputs.
3. From my observation, System Validation takes the trained model and gives 'tuned' predicted values of any dataset that is used for validation.
4. In this regard, it is my assumption that by inputting the full 30 reference data sets would give me tuned versions of themselves as tuned output values, which I can re-inject into Design Expert to obtain a 'tuned' regression equation, to be used for MATLAB's genetic algorithm optimisation.
5. After trying this, I did get higher R squared values for the regression equation as well, which "kind" of confirmed that this works. However, I have not seen this be done anywhere, nor do I know if my interpretation of how to use the
system validation via the fuzzy logic designer to get these tuned outputs is correct.
I've also attached a code script that was formulated without the use of the Fuzzy Logic Designer tool to try and ascertain the problem. The 'idea' centres around re-integrating the 30 datasets into the validation step this time (even though 26 of them were used to train in the first place), but for the purpose of obtaining tuned versions of themselves before using the tuned versions in Design Expert for generating a 'tuned' regression equation for MATLAB's GA Optimisation. Does this make sense?
I am unsure if these are the right steps. Any advice would be greatly appreciated.

I am working on flexible job shop scheduling problem, which I want to solve using a hybrid algorithm like VNS based NSGA-II or TS/SA + NSGA. Can I use Pymoo to implement this problem? Pymoo has specific structure to call the built-in algorithms, but in case of customization and using two algorithms how can I use pymoo framework?
Optimization by genetic algorithm (GAO) in electricity and smart grid?
why we use it ?
what is the process of this algorithms ?
I am using a genetic algorithm to solve a multivariable optimization problem. The difficulty in exploring all the solutions is that the permissible set of each variable of the solution is of the form {0} U [a,b], where 0 < a < b (the magnitudes are around a=4 and b=15). "Solutions" that do not satisfy the constraints get a low fitness. So when the genetic explores the search space, it is difficult that it tries solutions with one variable at 0 (zero). I can try to enlarge the interval around 0 and to modify the fitness of variables close to zero. Does anybody know how to treat this kind of constraints? By the way I am using the DEAP genetic algorithms, more precisely this one: http://deap.gel.ulaval.ca/doc/default/examples/ga_onemax.html.
I constantly use genetic algorithm and neural network , if you know and examine a better method to find when the data is high dimensional .
I have a thesis right now, I don't know if its possible to do, I'm trying to create a website builder, but instead of using Draggable Pre-Templates or Libraries, I would make a UI Component Generator with Different Properties and Designs.
But as I did some research, I realized its going to be messed up upon generation, I wanted it Linear in sequence and not just random Components with Random Designs, I wanted an organized linear pattern of generated UI Components. and I was thinking of using Seeds to find previously generated UI Components and saving it in a History Panel of panel. and being able to search it.
Needs some opinions and ideas because we're blasting our way to graduation..
Thank you! any help is appreciated!
If ChatGPT is merged into search engines developed by internet technology companies, will search results be shaped by algorithms to a greater extent than before, and what risks might be involved?
Leading Internet technology companies that also have and are developing search engines in their range of Internet information services are working on developing technological solutions to implement ChatGPT-type artificial intelligence into these search engines. Currently, there are discussions and considerations about the social and ethical implications of such a potential combination of these technologies and offering this solution in open access on the Internet. The considerations relate to the possible level of risk of manipulation of the information message in the new media, the potential disinformation resulting from a specific algorithm model, the disinformation affecting the overall social consciousness of globalised societies of citizens, the possibility of a planned shaping of public opinion, etc. This raises another issue for consideration concerning the legitimacy of creating a control institution that will carry out ongoing monitoring of the level of objectivity, independence, ethics, etc. of the algorithms used as part of the technological solutions involving the implementation of artificial intelligence of the ChatGPT type in Internet search engines, including those search engines that top the rankings of Internet users' use of online tools that facilitate increasingly precise and efficient searches for specific information on the Internet. Therefore, if, however, such a system of institutional control on the part of the state is not established, if this kind of control system involving companies developing such technological solutions on the Internet does not function effectively and/or does not keep up with the technological progress that is taking place, there may be serious negative consequences in the form of an increase in the scale of disinformation realised in the new Internet media. How important this may be in the future is evident from what is currently happening in terms of the social media portal TikTok. On the one hand, it has been the fastest growing new social medium in recent months, with more than 1 billion users worldwide. On the other hand, an increasing number of countries are imposing restrictions or bans on the use of TikTok on computers, laptops, smartphones etc. used for professional purposes by employees of public institutions and/or commercial entities. It cannot be ruled out that new types of social media will emerge in the future, in which the above-mentioned technological solutions involving the implementation of ChatGPT-type artificial intelligence into online search engines will find application. Search engines that may be designed to be operated by Internet users on the basis of intuitive feedback and correlation on the basis of automated profiling of the search engine to a specific user or on the basis of multi-option, multi-criteria search controlled by the Internet user for specific, precisely searched information and/or data. New opportunities may arise when the artificial intelligence implemented in a search engine is applied to multi-criteria search for specific content, publications, persons, companies, institutions, etc. on social media sites and/or on web-based multi-publication indexing sites, web-based knowledge bases.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
If ChatGPT is merged into search engines developed by online technology companies, will search results be shaped by algorithms to a greater extent than before, and what risks might be associated with this?
What is your opinion on the subject?
What do you think about this topic?
Please respond,
I invite you all to discuss,
Thank you very much,
Best wishes,
Dariusz Prokopowicz

Hello experts
I am dealing with an optimization problem in which the algorithm will choose the section of the column (RC structure) between a lower and an upper bound (e.g., LB and UB).
How can I ask the algorithm to change the cross section if the condition of the period isn't satisfied?
N.B: the period value is retrieved from the sap2000 software using the API, and it has no relation with the design variables to be set as a constriant.
Any one knows how to do it please?
Hi everyone,
I am a Ph.D. student and I am working on the production scheduling of an open-pit mine by genetic algorithm, and I have a problem with converting infeasible sequencing to a feasible one, so I need someone to help me. how can I create a normalized plan based on a completely random solution? on the other hand, how can I change the time periods that are assigned to genes of the chromosomes or all blocks of the ultimate pit?
I don't know any normalization method for obtaining a feasible sequencing plan, and I studied some papers related to it, but the normalization methods the authors used, have not been stated in detail! because when it is assigned a time period to blocks, there is any rule to regard the condition of 45 degrees on slopes!!!
I want to explain more about the MATLAB code I wrote. Precedence constraints (These constraints ensure that a block can only be extracted during some time period t if all of its predecessor blocks have been mined completely before or during time period t) are not true, I think because when I import the results into Surpac software, the extraction of blocks and sequencing of them seems like strip mining regardless of 45 degrees on slopes and instead of formation of inverse cones! I don't know how can I implement the normalization process you mentioned above?!
I attach an output file of MATLAB so you can see.
Thank you in advance
Please I need help me with some open research problems in genetic algorithm relative to computer vision.
Hello,
I am trying to build an (r,Q) inventory control model , to be implemented in a local manufacturing firm. In my model, r is identified by studying the demand trends and fitting normal distributions, while Q is identified by genetic algorithm minimising an inventory cost function.
Firstly the list of parts for re-order are first ideintified, after which a genetic algorithm co-optimises the batch sizes across all parts on re-order, considering factory capacity and demand constraints.
In my simulations, I find that the Q values suggested by my algorithm are realistic and in-line with what is required, however, it turns out that the model identifies all the parts which are less likely to be ordered by the production team.
Would anyone be able to shed any light on how to control this ?
I am studying an article about implementation of genetic algorithm. I have uploaded the parameters for this implementation.
As I know, in roullete selection method, probability of selecting individuals is based on their cost function values. the more optimum individual has the higher probability of being selected. So, what is the meaning of selection probability here?
Here is the link to the article:

Dear All,
Could you please inform regarding software for the metaheuristic algorithms that are compatible with Ms. Excel (add-ins) and free (or not).
a) Genetic algorithm (GA) = ???
b) Particle swarm-optimization (PSO) = ???
c) Bee colony optimization (BCO) = ???
d) etc..
I have been searching for genetic algorithm a lot, But I could not understand necessity of using crossover and mutation simultaneously yet. In an online course, following paragraph was written:
crossover is an operation which drive the population towards a local maximum(or minimum). If we use only crossover, it will yield approximately the same result as hill-climbing algorithm!!!mutation is a so-called divergence operation force one or more individuals of the population to discover other regions of the search space. So, this is essential in order to find the global optimum.
I can not understand it easily, especially because in metaheuristic algorithms, we must cope with somehow statistical-based optimization. Moreover, I had implemented GA in python and still can not realize the performance difference between these two parameters.
Hi everyone,
I am a Ph.D. student and I am working on the production scheduling of an open-pit mine by genetic algorithm, and I have a problem with converting infeasible sequencing to a feasible one, so I need someone to help me. how can I create a normalized plan based on a completely random solution? on the other hand, how can I change the time periods that I assigned to genes of the chromosomes or all blocks of the ultimate pit? I don't know any normalization methods for obtaining a feasible sequencing, and I studied some papers related to it, but the normalization methods the authors used, have not been stated in detail!
I would appreciate your guidance and help, in advance!
Over the last few decades, there have been numerous metaheuristic optimization algorithms developed with varying inspiration sources. However, most of these metaheuristics have one or more weaknesses that affect their performances, for example:
- Trapped in a local optimum and are not able to escape.
- No trade-off between the exploration and exploitation potentials
- Poor exploitation.
- Poor exploration.
- Premature convergence.
- Slow convergence rate
- Computationally demanding
- Highly sensitive to the choice of control parameters
Metaheuristics are frequently improved by adding efficient mechanisms aimed at increasing their performance like opposition-based learning, chaotic function, etc. What are the best efficient mechanisms you suggest?
Dear friend:
To reduce feature dimension and improve the model performance in machine learning, I am using the genetic algorithm to chose the best 20 features form 100 feature subset. However, some feaures are presented more than one in a chromosome, what is the best method to deal with this issue?
Best wishes!
WangChao
Hello experts
This question is shared with one of my research team.
We are dealing with an optimization problem in which the algorithm will choose the cross-section of the column (RC structure) between a lower and an upper bound (e.g., LB and UB).
We know that the obtained optimal values for the section can be for example 330mm.
Recently, we came across this phrase in a paper dealing with the same problem where the author stated that 'The column sections are square with dimensions of 250 mm × 250 mm to 1200 mm × 1200 mm, by a step of 50 mm'
Our concern is how to oblige the algorithm to choose 50mm for the next candidate within the design interval.
Anyone knows how to do it, please?
How should the learning algorithms contained in ChatGPT technology be improved so that the answers to questions generated by this form of artificial intelligence are free of factual errors and fictitious 'facts'?
How can ChatGPT technology be improved so that the answers provided by the artificial intelligence system are free of factual errors and inconsistent information?
The ChatGPT artificial intelligence technology generates answers to questions based on an outdated set of data and information downloaded from selected websites in 2021. In addition, the learning algorithms contained in ChatGPT technology are not perfect, which means that the answers to questions generated by this form of artificial intelligence may contain factual errors and a kind of fictitious 'facts'. A serious drawback of using this type of tool is that the ChatGPT-generated answers may contain serious factual errors. When people ask about something specific, they may receive an answer that is not factually correct. ChatGPT often answers questions eloquently, but often its answers may not relate to existing facts. ChatGPT can generate a kind of fictitious 'facts', i.e. the generated answers may contain stylistically, phraseologically, etc. correctly formulated sentences containing descriptions and characteristics of certain objects presented as real existing but not actually existing. In the future, a ChatGPT-type system will be refined and improved, which will be self-learning and self-improving in the analysis of large data sets and will take into account newly emerging data and information to generate answers to the questions asked without making numerous mistakes as is currently the case.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
How should the learning algorithms contained in the ChatGPT technology be improved so that the answers generated by this form of artificial intelligence to the questions asked are free of factual errors and fictitious "facts"?
How can ChatGPT technology be improved so that the answers provided by the artificial intelligence system are free of factual errors and inconsistent information?
What do you think about this subject?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Best regards,
Dariusz Prokopowicz

How to use a genetic algorithm for optimizing the Artificial Neural Network model in R.
In genetic algorithms, if two parents are two graphs or two trees, instead of two sequences, then how to design the crossover operator?
How can I calculate ANOVA table for the quadratic model by Python?
I want to calculathe a table like the one I uploaded in the image.

Few researches have stated that a multi population genetic algorithm avoids premature convergence as opposed to the conventional GA. Does multipopulation GA offer any other advantages over the single population GA? Also, is using a large population size for GA better than multipopulations?
I am trying to use the Genetic Algorithm to optimise the strategies for containing the spread of COVID-19. The strategies include (i) travel restrictions, (ii) lock-down, and (iii) wearing of nose masks. The model is formulated as an SEIR compartmentalized model.
I have spent a lot of time trying to learn how to do that using MATLAB but I have not succeeded.
I will be grateful if anyone can share their MATLAB codes with me.
How to use genetic algorithm in predicting some values based on csv file
Dear community,
I am planning on conducting a GWAS analysis with two groups of patients differing in binary characteristics. As this cohort naturally is very rare, our sample size is limited to a total of approximately 1500 participants (low number for GWAS). Therefore, we were thinking on studying associations between pre-selected genes that might be phenotypically relevant to our outcome. As there exist no pre-data/arrays that studied similiar outcomes in a different patient cohort, we need to identify regions of interest bioinformatically.
1) Do you know any tools that might help me harvest genetic information for known pathways involved in relevant cell-functions and allow me to downscale my number of SNPs whilst still preserving the exploratory character of the study design? e.g. overall thrombocyte function, endothelial cell function, immune function etc.
2) Alternatively: are there bioinformatic ways (AI etc.) that circumvent the problem of multiple testing in GWAS studies and would allow me to robustly explore my dataset for associations even at lower sample sizes (n < 1500 participants)?
Thank you very much in advance!
Kind regards,
Michael Eigenschink
i am using genetic algorithm. when i am testing its performance, i found my code doesn't work as it should be. when I am changing the weights from 1 to -1 (Maximize to minimize), my code shows the exact same results.
my code is as follows:
def train_evaluate(individual_solution):
x_bits = BitArray(individual_solution[0:4])
y_bits = BitArray(individual_solution[4:])
x = x_bits.uint
y = y_bits.uint
return x+y ,
population_size = 6
num_generations = 10
gene_length = 8
creator.create('FitnessMin', base.Fitness, weights = (-1.0,)) ## I only change this parameter
creator.create('Individual', list , fitness = creator.FitnessMin)
toolbox = base.Toolbox()
toolbox.register('binary', bernoulli.rvs, 0.5)
toolbox.register('individual', tools.initRepeat, creator.Individual, toolbox.binary, n = gene_length)
toolbox.register('population', tools.initRepeat, list , toolbox.individual)
toolbox.register('select', tools.selRoulette)
toolbox.register('mate', tools.cxOrdered)
toolbox.register('mutate', tools.mutShuffleIndexes, indpb = 0.2)
toolbox.register('evaluate', train_evaluate)
population = toolbox.population(n = population_size)
r, log = algorithms.eaSimple(population, toolbox, cxpb = 0.4, mutpb = 0.1, ngen = num_generations, verbose = False)
tools.selBest(population,k = 1)
when I test weights = (1, )
the result is: [[1, 1, 1, 1, 1, 1, 1, 1]]
when I test weights = (-1, )
the result is: [[1, 1, 1, 1, 1, 1, 1, 1]]
great thanks if you could kindly help me.
Hello,
I found this approach -the per-node approach- in a paper recently published, in which the author tried to describe a new way to tick a behaviour tree instead of using the conventional approach, in order to evolve a behaviour for an agent (game agent) using genetic programming.
I would like to discuss this approach and how it works deeply.
If anyone could share any idea about this, it will be more productive.
Thank you.
In his name is the judge
Hi
There is a fuzzy logic control system in python. The system contain 2 inouts and 18 outputs.
inference of system is mamdani and shape function used to be guassian.
Then in term of refine performance of the controller I need to optimize specifications belong to shape functions of both input and output. In order to that I need to use multi objective optimization.
We have 2 input and 1 output in case of this problem. I have developed 3 shape functions for each entrance and 3 for output and the shape function is gaussian so we have 18 parameters totally.
I defined my problem as a function in python. But notice this I there is not any clear relationship between input and output of function. It’s just a function which is so complicated with 2 inputs and 18 outputs.
I made my decision to use NSGAII algorithm and I really don't want to change the algorithm.
So I try every way to optimize my function but I didn’t find any success. By searching about python library which can do multiobjective optimization I find Pymoo as the best solution but I really failed to optimize my function which is complicated custom function, with it.
So It’s really my pleasure if you can introduce me a library in python or suggest me a way that I can use Pymoo in order to this aim.
wish you best
Take refuge in the right.
Hello,
I'm working with a large number of L*a*b* colour points from ceramic samples, and I'm interested in reconstructing possible spectrums that could have made those colours. There's more than one possibility, of course, but my plan was to use a genetic algorithm approach to find several answers at a distance from each other to set out the solution space. The better candidates are all positive, and reasonably smooth as I'm dealing with natural clays, not sharply pigments goods.
The problem is that I'm currently unable to get closer than about 3units to the target colour, and often no closer than 10-15 units. This could be for the following reasons, is there anything else I've missed?
1) Solution space pixelation - I'm working with 5nm wide bands, so there are only 81 bands in the spectrum, only a fraction of which matter for the specific colour, and it's possible that there's no close solution at this resolution
2) many local maximas - it's possible the solution space is very unsmooth, and the algorithm is latching onto the 'wrong' one too early. The evidence for this is that runs frequently 'lock' quite early on, but repeated runs give different results.
3) something else...
Hi,
What is the different between 'fitness function score' with 'objective function value'? If it is different, how we define the score? I use single objective GA run using Matlab optimization tool. I only have bounds for my variables. Thank you
Although the genetic algorithm is very helpful to find a good solution, it is very time-consuming. I've recently read an article in which the authors used the genetic algorithm for pruning a neural network on a very small dataset. They pruned a third of their network and reduces the size of the model from 90MG to 60MG. Their pruning also decreased the inference time of their model by around 0.5 seconds. Although, unlike other methods, pruning using the genetic algorithm does not deteriorate their model's performance, finding redundant parts of the network using the genetic algorithm is an overhead. I wonder why they think their method is useful and why people still use the genetic algorithm.
I want to use pyomo to model MILP problems in python such as Green Vehicle Routing Problem and others but use metaheuristics algorithms (GA, Harmony search, etc.) to solve those problems instead of using pyomo solvers, its part of my phd project and can not use derivative-based solvers at all; If the answer that I can not, what is the alternative to model the problems in python then solve them with metaheuristics algorithms because i found pyomo easy and time saving to be used in formulating the problems.
Hi,
How can we prove the results of the GA that are optimised, in other words, are there criteria or measures to determine the best results (optimize results).
regards
MATLAB optimization tool box. nothing mentioned in the matlab documentation.
Need to know the procedure of implementation as various options are available in CST for optimization.
I have studied in research paper adaptive genetic algorithm, adaptive firefly algorithm but the clear description is not available in those research papers. Plz help me so that I can clarify this and why it is used ?
Is there an index that includes the mixing index and pressure drop for micromixers?
For example, for heat exchangers and heat sinks, there is an index that includes heat transfer performance and hydraulic performance, which is presented below:
η=(Nu/Nub)/(f/fb)1/3
The purpose of these indexes is to create a general criterion to check the devices' overall performance and investigate the parameters' impact.
Is there an index that includes mixing performance and hydraulic performance for micromixers?
Hello,
When I should use memetic algorithm rather than genetic algorithm?
Best Regards,
How does one optimize a set of data which is comprised of 3 input variables and 1 output variable (numerical variables) using a Genetic Algorithm? and also how can I create a fitness function? How is a population selected in this form of data? What will the GA result look like, will it be in a form of 3 inputs and 1 output?
I do understand how the GA works, however, I am confused about how to execute it with the form of data that I have.
My data is structured as follows, just for better understanding:
3 columns of Input data, and the fourth column of Output Data. (4 Columns and 31 rows) data. The 3 input variables are used to predict the fourth variable. I want to use GA to improve the prediction results.
Lastly, Can I use decimal numbers (E.g. 0.23 or 24.45); or I should always use whole numbers for chromosomes.
I have running GA optimization using "gamultiobj" in Matlab. The upper bound and lower bound of my design variables are like [10 30 175 1] and [30 60 225 3]. But after convergence, I see the design variables (like all of them) near 20,47,175 and 1. I am not getting a Pareto Front. What could be the possible reasons for that?
how genetic algorithm fittness function calculated for botnet attack detection?
I already saw some examples of GA (genetic algorithm) applications to tune PID parameters, but I (until now) don't know a way to define the bounds. The bounds are always presented in manuscripts, but they appear without great explanations. I suspect that they are obtained in empirical methods.
Could Anyone recommend me research?
Assuming, I have a number of vectors where each item in the vector is a real number. I was to generate some vectors from those initials ones and add them to the collection of initial vectors. This was I can have a initial population of increased size. What are some ways to do this?
Dear all,
according to [1], QGA are more efficient than GA (genetic algorithms), and according to [2] PSO is better than GA. Do you know if there are papers that do the comparison between PSO and QGA ?
Thank you
[1] - Z.Laboudi (2012) - Comparison of Genetic Algorithm and Quantum Genetic Algorithm
[2] - F D Wihartiko (2017) - Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus
time tabling problem
How can I optimize ANFIS using Genetic Algorithm; and also with Aquila Optimizer in MATLAB? Any available code I can use?
Dear Members
I had recently gone through a sample program code (in MATLAB) corresponding to Pareto GA in Appendix II (Program 4, Page: 219) of the book PRACTICAL GENETIC ALGORITHMS by Haupt. However, I failed to understand the Niching strategy employed in the program code. Also, kindly explain the Pareto dominance tournaments that have been employed or used for mate selection in the example provided in the book. The flow diagram for Pareto GA delineated in Figure 5.2 of the book, mentions an increase in the cost of closely located chromosomes. But the code apparently contains no such steps. Kindly explain the anomaly.
hello!
how i can use the Neural network fitness function i.e., after simulation the value we get basically in terms of MSE(mean squared error). so can you tell me how we this MSE as fitness function in Genetic Algorithm in order to minimize the MSE for better neural netwrok model.
hello!
can anyone tell me how i can integrate the neural network and genetic algorithm. i have a simple real coded genetic algorith where i need to use the neural network. please help me on thia. my code is in matlab. if some one provide me the code
How can I get a MATLAB code for solving multi objective transportation problem and traveling sales man problem?
Here, I have implemented my genetic algorithm with combining two crossover operators i.e..(PMX,OX). After running the algorithm I am getting same fitness values as output from staring generation to ending generation. There is no change Is it correct.? please can anyone answer
Best regards
Anna
Hii,
I have implemented genetic algorithm with partially mapped crossover for solving tsp. Here I'm using different random.seed vlaues(24,42,72,112). When I run the algorithm , can I get the same graph with different seed values without changing the parameters but city co-ordinates are randomly generated.
Please can anyone answer..
best regards
Annapareddy
Hello all, I am new to image processing.... I want to detect edges from an image. When I searched on detecting edges in images I came across about edge detection using genetic algorithms. Where I find the chromosomes and find fitness function for crossover and mutation process. Also, they say that chromosomes are binary array. I am finding it difficult to understand how can we find binary arrays from an image and picking that as a chromosomes or do we have any methods to find chromosomes from an image. Please help me...any help is appreciated.
Hii,
I have implemented genetic algorithm with partially mapped and ordered crossover for solving tsp.
In partially mapped crossover, I have taken 100 generations, when I run the algorithm the best solution is getting of 20th generation. from 20th generations onwards the solution is constant until last generation( 100). may I know the error . Please acn anyone suggest the error.?
Thank you
Define attributes suitable for comparison of both algorithms?
Genetic algorithm with tabu search OR Genetic algorithm with simulated annealing ?
I am using an evolutionary-based GA optimisation tool that divides the space of possibilities into subsets to perform hypercube sampling within each subset, and thereafter generates multiple generation of results, the best of which will form the Pareto front and through each iteration move closer to the global optimum. However is it possible that through the process of hypercube sampling (and hence disregarding some options) , this tool might miss a true global optimum?
While working in both the software, after loading the training and validation data for the prediction of a single output using several input variables (say 10), it skips some of the inputs and delivered an explicit mathematical equation for future prediction of the specific parameter but it skips some of the input variables (say 2 or 3 or maybe greater). What criteria are these software uses in the back for picking the most influential parameters while providing a mathematical predictive model?
Hello everyone,
Could you recommend papers, books or websites about mathematical foundations of artificial intelligence?
Thank you for your attention and valuable support.
Regards,
Cecilia-Irene Loeza-Mejía