Questions tagged [genetic-algorithms]

For questions related to genetic algorithms (GAs), which are a form of evolutionary algorithms. A genetic algorithm is a method (more precisely, a metaheuristic) for solving optimization and search problems based on natural selection processes (that is, they use bio-inspired operators such as mutation, crossover, and selection).

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Write $k-$Means as a Genetic Algorithm (Genetic Clustering Algorithm) [closed]

I'm trying to write $k-$Means a genetic algorithm but i'm having trouble with the chromosome representation of the algorithm. I'm thinking in represent $k-$Means as a string (chromosome) with $n$ ...
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1answer
36 views

What exactly is the population in the problem of finding the best path in a network of nodes using genetic algorithms?

I have 17 nodes in my network with 3000 different paths in total. I have to select the path with highest available bandwidth, using genetic algorithm. I'm confused about the approach! Should I have ...
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35 views

In NEAT, how do node numbers work?

I have read a lot of debates about node ids and such. I'm not 100% sure how it works, but I am assuming the next node added to a network would be the next number in that specific networks list? For ...
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40 views

How does the paper implement NEAT without a global set tracking Innovations?

I have been reading this paper on NEAT and trying to implement the algorithm in C#. For the most part, I understand everything in the paper however, there are 2 things I don't understand that confuse ...
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21 views

Is NEAT speciation really effective?

I tried implementing NEAT algorithm from scratch, and it successfully solves XOR problem. I followed the original NEAT paper. However, when I run XOR problem solving test and calculate average ...
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1answer
37 views

Does elitism cause premature convergence in genetic algorithms?

I have a genetic algorithm which is working fairly well. It's got all the standard operators, including initial random population, crossover ratio, mutation rate, degree of mutation, etc. This works ...
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1answer
56 views

Is a genetic algorithm efficient for a snake game?

I am working on a DIY project in which I want to be able to train a neural network to play Snake. Is a genetic algorithm an efficient way of training a network for this application? For a GA, what ...
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1answer
40 views

What is the most computationally efficient genetic algorithm?

In researching genetic algorithms, it seems that there are various methods of selection and other operator methods that can significantly change the performance. For example, this picture contains ...
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1answer
38 views

How to design fitness function for multiple objectives?

I am currently building a neural network with genetic algorithms that learns to fly a 2D drone to a target. My goal is that it achieves all tasks as fast as possible, but I want the drone to also fly ...
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1answer
21 views

How to handle equality constraints in the mutation operation of evolutionary algorithms?

I am new in evolutionary algorithms field. I have a chromosome of 6 variables (real variable), where the sum of these variables is equal to 1. I am looking for mutation formulas that can generate a ...
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1answer
60 views

Use Reinforcement Learning instead of genetic algorithm for optimization

I want to use RL instead of genetic or any other evolutionary algorithm in order to find the best parameter for a function. Here is the problem: Given a function $$f(x,y,z,data)$$ x,y and z are some ...
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56 views

How to evaluate a Genome in NEAT

I am trying to implement NEAT from scratch by going through the original NEAT paper. I implemented a Genome class which consists of a list of Node Genes and ...
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42 views

How should the 1-point crossover and mutation be defined for the problem of finding the largest circle that does not enclose any point?

For a random scattering of points, in a bounded area, the goal is to find the largest circle that can be drawn inside those same bounds that does not enclose any points. Solving this problem with a ...
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How to choose a mutant solution with a genetic algorithm in a localization problem?

I am new to genetic algorithm but I understand the concept of mutations when taking continuous parameters for an evolutionary algorithm. But I can't get it with a discrete one. For isntance, let's say ...
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Do Learning Classifier Systems extend beyond reinforcement learning?

In 2000 the John Holland wrote concerning Learning Classifier Systems (LCS) In recent years there has been a focus on classifier systems as performance systems or evolutionary incarnations of ...
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70 views

Genetic Algorithm Tetris Python not improving

I'm trying to create an AI using a Neural Network a Genetic Algorithm to learn how to play tetris, but it looks like something is wrong because, even after 20 generations, i can't see any improvement. ...
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79 views

How can I select features for a symbolic regression problem to be solved with genetic programming?

I want to solve a symbolic regression problem with genetic programming. My dataset is similar to this one, but I have 30 features, and I want to use only the most sensitive features. I found this ...
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1answer
53 views

How to deal with evolutionary/genetic fitness function that can have both negative and positive values?

I am optimising function that can have both positive and negative values in pretty much unknown ranges, might be -100, 30, 0.001, or 4000, or -0.4 and I wonder how I can transform these results so I ...
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30 views

If one of the inputs to a neural network (that represents a policy) is noisy and degrades the performance, would this architecture solve the issue?

I'm using genetic algorithms to train deep reinforcement learning (DRL) agents, similarly to what was done in this paper. DRL policies are therefore represented by deep neural networks, which map ...
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1answer
48 views

How to design a fitness function for a problem where there are 2 objectives?

I am told to express a fitness function for a question I have been presented. I am unsure how I would express the function. In words, what I have written down makes sense but turning this into a ...
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1answer
57 views

Are Genetic Algorithms suitable for problems like the Knuth problem?

We all know that Genetic Algorithms can give an optimal or near-optimal solution. So, in some problems like NP-hard ones, with a trade-off between time and optimal solution the near-optimal solution ...
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2answers
35 views

How to detect that the fitness landscape of a genetic algorithm is changing over time?

I understand that in each generation of a genetic algorithm, that generation must re-prove it's fitness (and then the fittest of that population is taken for the next population). In this case, I ...
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2answers
53 views

Are there any disadvantages to using a variable population size in genetic algorithms?

When implementing a genetic algorithm, I understand the basic idea is to have an initial population of a certain size. Then, we pick two individuals from a population, construct two new individuals (...
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1answer
56 views

Is it possible that the fittest individuals in an Artificial Life population may be successful by not actively pursuing the rules of the environment?

I'm trying to understand Artificial Life (e.g. here for a simple background) in Computational Evolution. I understand that in this set of methods, you set up a dynamic environment (e.g. the ecology of ...
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1answer
54 views

What is the impact of changing the crossover and mutation rates?

What is the impact of using a: low crossover rate high crossover rate low mutation rate high mutation rate
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What would be the best criteria for determining the best generation on a maze training by mutation?

I'm training a neural network to solve a maze. My process is the following: Randomly generate a small maze Spawn hundreds of cars at the start, that will go through the same one maze Assign the same ...
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1answer
34 views

Is there some known pattern for selecting a batch of candidates for the next generation?

I'm a beginner with a classic "racing car" sandbox and a homemade simple neural network. My pattern: Copy the "top car" (without mutation) to the next generation If there are ...
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1answer
40 views

What is the difference between sensitivity analysis and parameter tuning?

I tried different values of genetic algorithm operators: many crossover rates from 20% to 80% many crossover rates from 1% to 20% varying the population size The study of different parameter values ...
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44 views

Genetic algorithm stuck and cannot find an optimal solution

I'm working on SLAP (storage location assignment problem) using genetic algorithm implemented manually in the C++ programming language. The problem is fairly simple, we do have ...
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37 views

Unable to meet desired mean squared error

I wish to get MSE < 0.5 on test data (https://easyupload.io/zr7xf3) which is 20% of given data chosen randomly. But I am reaching 0.73 using both plain Ridge Regression as well as a neural network ...
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2answers
121 views

Is it possible to perform neuroevolution without a fitness function?

My question is about neuroevolution (genetic algorithm + neural network): I want to create artificial life by evolving agents. But instead of relying on a fitness function, I would like to have the ...
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1answer
44 views

Measuring novel configuration of points

I am trying to implement Novelty search; I understand why it can work better than the standard Genetic Algorithm based solution which just rewards according to the objective. I am working on a problem ...
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27 views

Which 6-bit string would represent an optimal solution for trap-3 in the Linkage Learning Genetic Algorithm?

I am struggling to learn certain Evolutionary algorithm concepts and also relations between each of them. I am going through the Linkage Learning Genetic Algorithm (LLGA) right now and came across ...
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1answer
69 views

What are trap functions in genetic algorithms? [duplicate]

What are trap functions in genetic algorithms? Suppose you ran a GA with a trap function and examined the population midway through the run. Can someone explain what you would expect the population to ...
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1answer
44 views

What are most commons methods to measure improvement rate in a meta-heuristic?

When I run a meta-heuristics, like a Genetic Algorithm or a Simulated Annealing, I want to have a termination criterion that stops the algorithms when there is not any significant fitness improvement. ...
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2answers
121 views

Can we use genetic algorithms to evolve datasets?

Genetic algorithms are used to solve many optimization tasks. If I have a dataset, can I evolve it with a genetic algorithm to create an evolved version of the same dataset? We could consider each ...
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experiences on using genetic algorithms as a way to improve neural networks?

I wonder if there is research, patents, or libraries using Genetic algorithms (GA) to improve Neural Networks. I don't find anything in the subject. For example: use GA to find better parameters in a ...
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3answers
95 views

How to avoid running out of solutions in genetic algorithm due to selection?

The genetic algorithm consists of 5 phases of which 4 are repeated: Initial population (initially) Fitness function Selection Crossover Mutation In the selection phase, the number of solutions ...
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2answers
235 views

What is meant by gene, chromosome, population in genetic algorithm in terms of feature selection?

I am trying to understand the genetic algorithm in terms of feature selection and these features are extracted using a machine learning algorithm. Let's suppose I have data of heart rate for 3 minutes ...
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27 views

Crossover method for gene value containing a set of values

I have a chromosome where each gene contain s set of values. Like the following: chromosome = [[A,B,C],[C,B,A],[C,D,],[],[E,F]] The order in each gene values matters. (A,B,C is different to A,C,B) ...
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1answer
851 views

What is the difference between reinforcement learning and evolutionary algorithms?

What is the difference between reinforcement learning (RL) and evolutionary algorithms (EA)? I am trying to understand the basics of RL, but I do not yet have practical experience with RL. I know ...
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32 views

How to effectively crossover mathematical curves?

I'm trying to optimize some reflective properties of curves of the form: $a_1x^n+a_2x^{n-1}+a_3x^{n-2} + ... + a_n + b_1y^n+b_2y^{n-1}+b_3y^{n-2} + ... + b_n = 0$ which is basically the curve that ...
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2answers
69 views

How do I determine the genomes to use for crossover in NEAT?

If I have the fitness of each genome, how do I determine which genome will crossover with which, and so on, so that I get a new population? Unfortunately, I can't find anything about it in the ...
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1answer
44 views

How does the crossover operator work when my output contains only 2 states?

I'm currently working on a project where I am using a basic cellular automata and a genetic algorithm to create dungeon-like maps. Currently, I'm having an incredibly hard time understanding how ...
2
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1answer
136 views

Genetic Algorithm Python Snake not improving

So, i have created Snake game using Pygame and Python. Then i wanted to create an AI with Genetic algorithm and a simple NN to play it. Seems pretty fun, but things aren't working out. This is my ...
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2answers
56 views

What does it mean if classification error is equal between two networks but the MSE is different?

I'm experimenting with training a feedforward neural network using a genetic algorithm and I've done a few tests using both the mean squared error and classification error functions as fitness ...
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0answers
276 views

How to perform classification with NEAT-Python?

I am trying to do classification using NEAT-python for the first time, and I am having difficulty getting the accuracy rate. I tried the same problem with an ANN and was able to get a good accuracy ...
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1answer
76 views

What is the difference between evolutionary computation and evolutionary algorithms?

A book on evolutionary computation by De Jong mentions both the term evolutionary algorithms (EA) as well as evolutionary computation (EC). However, it remains unclear to me what the difference ...
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35 views

Using ML for Enemy Generation in Video Games

I am attempting to make a 2-D platformer game where the player traverses through an evil factory that is producing killer robots. The robots spawn at multiple specific locations in each level and ...
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2answers
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Are connections genes in a genome ever deleted or just disabled?

When a new node is added, the previous connection is disabled and not removed. Is there any situation in which a connection gene is removed? For example, in the above diagram connection gene with ...