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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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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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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Representation of real numbers in Genetic Algorithm

Take a look at section 2.2.2 of this book (from Page-15 to 16). 2.2.2 Representation and Evaluation $$max f (x)= x sin(10πx)+2.0 ... ... ... (2.8)$$ $$s.t. −1 ≤ x ≤ 2$$ We can use a ...
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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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Where or for what could genetic algorithms be used in the context of project management?

Where or for what could genetic algorithms (GA) be used in the context of project management (PM)? I thought about task dispatching, but I'm looking for other potential uses of GAs in the context of ...
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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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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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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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239 views

How to create and train (with mutation and selection) a neural network to predict the next state of a board?

I'm aiming to create a neural network that can learn to predict the next state of a board using the rules of Conway's Game of Life. Technically, I have three questions, but I felt that they needed to ...
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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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1answer
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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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59 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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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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Unable to achieve expected outputs using NEAT for the snake game

I am trying to implement NEAT for the snake game. My game logic is ready, which is working properly and NEAT configured. But even after 100 generations with 200 genomes per generation, the snakes ...
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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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222 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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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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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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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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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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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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Is there any disadvantage of the maximum number of fitness function call as a stop criterion?

I'm studying different stop criteria in genetic algorithms and the advantages and disadvantages of each of them for evaluating different algorithms. One of these methods is the max number of fitness ...
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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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Open-source tools or libraries to experiment with neural networks and evolutionary algorithms [closed]

I'd like to do some experimenting with neural net evolution (NEAT). I wrote some GA and neural net code in C++ back in the 90s just to play around with, but the DIY approach proved to be labor-...
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How to solve the problem of too big activations when using genetic algorithms to train neural networks?

I am trying to create a fixed-topology MLP from scratch (with C#), which can solve some simple problems, such as the XOR problem and MNIST classification. The network will be trained purely with ...
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What sort of game problems can neural networks trained/evolved with evolutionary algorithms solve, and how are they typically implemented?

I'm interested mostly in the application of AI in gaming; in case this adjusts the way you answer, but general answers are more than welcome as well. I was reading up on Neural Networks and combining ...
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Should I use neural networks or genetic algorithms to solve Gomoku?

Currently, I'm doing a project that's about creating an AI to play the game Gomoku (it's like tic tac toe, but played on a 1515 board and requires 5 in a row to win). I have already successfully ...
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How is the distance between pointers in Stochastic Universal Sampling determined?

I'm studying about different selection methods in genetic algorithms. My question is about the Stochastic Universal Sampling (SUS) selection method. I know that each individual will occupy a segment ...
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How to handle infeasibility caused due to crossover and mutation in genetic algorithm for optimization?

I have chromosomes with floating-point representation with values between $0$ and $1$. For example Let $p_1 = [0.1, 0.2, 0.3]$ and $p_2 = [0.5, 0.6, 0.7]$ be two parents. Both comply with the set of ...
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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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Does fitness proportionate selection select multiple individuals?

Does fitness proportionate selection select multiple individuals? So, I read on Wikipedia and on multiple Stack Exchange threads about fitness proportionate selection or rather roulette selection, but ...
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How can I calculate the “mean best fitness” measure in genetic algorithms?

I've just started to learn genetic algorithms and I have found these measurements of runs that I don't understand: MBF: The mean best fitness measure (MBF) is the average of the best fitness values ...
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Are there any other machine learning models apart from Reinforcement Learning and Q Learning to play video games?

OpenAI's Universe utilises RL algorithms and I have heard of some game-training projects using Q learning, but are there any others which are used to master/win games? Can genetic algorithms be used ...
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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 create a good fitness function?

In genetic algorithms, a function called "fitness" (or "evaluation") function is used to determine the "fitness" of the chromosomes. Creating a good fitness function is one of the challenging tasks in ...
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How to design a fitness function for the 8-queens problem?

In evolutionary computation and, in particular, in the context of genetic algorithms, there is the concept of a fitness function. The better a state, the greater the value of the fitness function for ...
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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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Given a list of integers $\{c_1, \dots, c_N \}$, how do I find an integer $D$ that minimizes the sum of remainders $\sum_i c_i \text{ mod } D$?

I have a set of fixed integers $S = \{c_1, \dots, c_N \}$. I want to find a single integer $D$, greater than a certain threshold $T$, i.e. $D > T \geq 0$, that divides each $c_i$ and leaves ...
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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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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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Is it possible to separately evolve a part of the population?

In a classic example of a genetic algorithm, you would have a population and a certain amount of simulation time to evaluate it and breeding. Then proceed to the next generation. Is it possible, ...
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What is an appropriate fitness function for a simulated self-driving car?

I have been working for ages on a neuro-evolution AI program, where cars learn how to race around a track. Presently, I have a rudimentary fitness function that awards points for every degree ...
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Have evolutionary algorithms been used for engineering design?

Recently, I've been looking recently into what uses AI - specifically evolutionary algorithms - may have in automating engineering design. For a long time, there have been algorithms that solve ...
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What is the difference between memetic algorithms and genetic algorithms?

What is the difference between memetic algorithms and genetic algorithms? Is an individual's lifetime a learning part of memetic algorithms?
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What are examples of optimization problems that can be solved using genetic algorithms?

I'm trying to learn how genetic algorithms can solve optimization problems. I have already learned how genetic algorithms can solve the knapsack, TSP and set cover problems. I'm looking for some other ...
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What evolutionary algorithms are there that model epigenetics?

What evolutionary algorithms are there that model or incorporate some notion of epigenetics? What are the pros/cons of those approaches? Are there vast insufficiencies or wide-open questions about ...
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What exactly are genetic algorithms and what sort of problems are they good for?

I've noticed that a few questions on this site mention genetic algorithms and it made me realize that I don't really know much about those. I have heard the term before, but it's not something I've ...