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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Predicting next position based on previous positions

I am trying to find a model to predict the next position of a robot based on a set of previous positions and values. The problem is: a robot starts at Step (0) and Position (0) and gives to this ...
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Why we need to do mutation after crossover? [closed]

I am reading about genetic algorithms. In the genetic algorithm process we perform crossover and mutation. However, in the crossover, we already produce offspring, so then why do we also need to ...
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Does pairing children with their parents cause any harm (in a genetic program)?

If you pair parents with their children (with a cross-over) does this prevent making individuals which are more fit or does this cause other side effects which are harmful to the genetic process? I ...
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How would you find the fitness function for the following puzzle?

A magic hexagon is a hexagon whose sum of each row and diagonal equals to a constant. I am currently searching a magic hexagon of order 3 with the magic constant = 38. I am using a genetic algorithm ...
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How to decode P bits that represent a random weight generator?

So I've been tasked by my neural network professor at university to replicate the following research: Intelligent Breast Cancer Diagnosis Using Hybrid GA-ANN. Each chromosome represents a possible net,...
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In NEAT, how do I prevent duplicate connections?

According to this paper, duplicate mutations are only given the same innovation numbers within the same generation. What do I do if a connection gets broken into 2 connections and a node during 2 ...
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How to calculate adjusted and normalized fitness when a higher raw fitness is better

I am reading Genetic Programming: On the Programming of Computers by Means of Natural Selection by John R. Koza. For calculating the "standardized fitness" of an individual, where a lower ...
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Why does the schema theorem of genetic algorithms hold?

I have been reading about the Schema Theorem - one of the first theorems from the field of evolutionary computing and genetic algorithms, largely responsible for justifying the use of genetic ...
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Would it be a good idea to mutate half of the offspring of each GA generation 100% of the time and the other half 0% of the time?

I was reading about genetic algorithms, and to my understanding a genetic algorithm (GA) is an algorithm that starts with an initial population of chromosomes, where each chromosome has associated ...
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Are Genetic Algorithms suitable for a problem with a non-unique optimal solution?

I was wondering if a genetic algorithm is useful if the optimization problem has several optimal solutions. My thought was that I should not use it since when combining two members of a population who ...
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Is there a crossover that also considers that every index in the vector also influences the fitness function?

Is there a crossover that also considers that every index in the vector also influences the cost function? I have two vectors $v_1=[A_1, A_2, A_3, A_4, A_5]$ and $v_2=[A_5, A_3, A_2, A_1, A_4]$. The ...
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In the NEAT algorithm, what is the purpose of treating disjoint and excess genes differently?

In the NEAT algorithm, what is the purpose of treating disjoint and excess genes differently? They are treated so (or may be treated potentially) at least when calculating the distance between 2 ...
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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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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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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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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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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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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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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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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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Is it possible to optimize a multi-variable function with a reinforcement learning method?

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 $$f(x,y,z, \text{data}),$$ where $x$, $...
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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 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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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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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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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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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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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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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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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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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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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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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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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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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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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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3 votes
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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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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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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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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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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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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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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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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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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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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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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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2 votes
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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 ...
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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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