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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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15 views

The disadvantage of maximum number of fitness function call as stop criteria

I'm studying different stop criteria in genetic algorithm and advantages and disadvantages of each of them for evaluating different algorithms. One of these methods is max number of fitness function ...
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An “elevator pitch” breakdown of areas of applications for Reinforcement Learning & Neural Networks vs. Genetic Algorithms

I'm looking for an "elevator pitch" breakdown of areas of applications for Reinforcement Learning & Neural Networks vs. Genetic Algorithms, both actual and theoretical. Links are welcome, but ...
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Distance between pointers in Stochastic Universal Sampling (SUS)

I'm studying about different selection methods in genetic algorithm. My question is about Stochastic Universal Sampling (SUS) selection method. I know that each individual will occupy a segment of the ...
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1answer
44 views

What are examples of optimization problems that can be solved using a genetic algorithm?

I'm trying to learn how a genetic algorithm can solve optimization problems. I have already learned how a genetic algorithm can solve knapsack, TSP and set cover problems. I'm looking for some other ...
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18 views

How to calculate multiobjective optimization cost for ordinary problems?

What I did: Created a population of 2D legged robots in a simulated environment. Found the best motor rotation values to make the robots move rightward, using an objective function with Differential ...
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Question about minimizing sum of remainders

I have a set of integers [$c_1$, $c_2$, $c_3$, ... , $c_N$]. A non-negative integer D, greater than a certain threshold, divides each 𝑐𝑖 and leaves remainder 𝑟𝑖,i.e., $r_i$ can be written as $r_i=...
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1answer
27 views

Metrics of quality of parameter space exploration

Considering a black box optimization problem on non-linear, non-convex function where we want to minimize an objective function. One way to assess the quality of an optimizer is to look at the best ...
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CNN - Visualizing images near decision boundary - Pixels inexplicably tend to edges

We are exploring the images classified by a CNN at its decision boundary, using Genetic Algorithms to generate them. We have created a fine-tuned binary grayscale image classifier for cats. As the ...
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1answer
33 views

Library for rendering neural network NEAT

I just finished my implementation of NEAT and I want to see the phenotype of each genome. Is there a library for displaying a neural network like this? Example of my genome syntax: ...
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37 views

How can I solve the linkage problem in genetic algorithms?

In a genetic algorithm, the order of the genes on a chromosome can have a significant effect on the performance (capacity to generate adaptation) of the genetic algorithm, where two or more genes ...
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Is a neural network the correct approach to optimising a fitness function in a genetic algorithm?

I've written an application to help players pick the optimal heroes during the draft phase of the Heroes of the Storm MOBA. It can be daunting to pick from 80+ characters that have synergies/counters ...
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2answers
165 views

Can neuroevolution be combined with gradient descent?

Is there any precedent for using a neuroevolution algorithm, like NEAT, as a way of getting to an initialization of weights for a network that can then be fine-tuned with gradient descent and back-...
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1answer
43 views

Disabling of genes during crossover (NEAT)

I am implementing NEAT (Neuroevolution of augmenting topologies) by Stanley, Original Paper. I am facing a problem during crossover of genomes. Suppose two networks with connections ...
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1answer
66 views

Can we automate the choice of the hyper-parameters of the evolutionary algorithms?

Certain hyper-parameters (e.g. the size of the offspring generation or the definition of the fitness function) and the design (e.g. how the mutation is performed) of evolutionary algorithms usually ...
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32 views

Can we use the Tierra approach to optimize machine code?

Thomas Ray's Tierra is a computer program which simulates life. In the linked paper, he argues how this simulation may have real-world applications, showing how his digital organisms (computer ...
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56 views

Exploding population size in neat-python

I am trying to make my AI win the board game "Catan" against my friends. Therefore i am using the python implementation of NEAT. As I changed the values of ...
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27 views

What qualifies as 'fitness' for a genetic algorithm that minimizes an error function?

Suppose I have a set of data that I want to apply a segmented regression to, fitting linearly across the breakpoint. I aim to find the offsets and slopes of either line and the position of the ...
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1answer
69 views

How does NEAT find the most successful generation without gradients?

I'm new to NEAT, so, please, don't be too harsh. How does NEAT find the most successful generation without gradient descent or gradients?
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1answer
299 views

Can we evolve 0 and 1?

Is it possible to combine or create conditional statements of 0 and 1, and optimize with an evolutionary algorithm (given that all computers use a binary system)? There may be an algorithm that maps ...
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181 views

How to implement a neural network for Flappy Bird in Python?

I am new in the field of AI. I am working to create the flappy bird using Genetic Algorithm. After reading and seeing some examples, I saw that most implementations use a Neural Network + Genetic ...
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1answer
55 views

How to find optimal mutation probability and crossover probability?

I have a genetic algorithm that maximizes a fitness function with two variables f(X,Y). I have been running the algorithm with various parameters in mutation and crossover probability (0.1, 0.2, ...) ...
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Shortest route GA: One loop through one dataset vs multiple loops through subsets of the same data?

I've a rather simple question for a school project. We're developing a GA solution for the following problem: Chromosome: A location with lat-lon coords. There are two types of locations - up to 15 ...
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52 views

Is there a reason evolutionary algorithms are language-bound in research material?

I've been working on genetic algorithms & evolutionary strategies for a while now in a research context. Across the vast majority of the articles and content I've read, every single one of them ...
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Applications of genetic algorithms in project management

Do you see any GA Application that could support Project Management? I thought about Task Dispatching. I am curious about your ideas.
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Judging a genetic algorithm's priority-based schedules by how far ahead the higher priority things are done

I'm creating a schedule for a summer camp. Because of the high risk of rain, the higher priority activities need to be attempted first, so there is more time for later attempts if need be (temporarily ...
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60 views

How do I write a genetic algorithm to solve the knapsack problem?

I am trying to write a genetic algorithm that generates 100 items, assigning random weights and utilities to them. And then try to pick items how out these 100 items while maximising the utility and ...
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1answer
45 views

How to handle infeasiblity 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 ...
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32 views

What is the genetic algorithm for? [duplicate]

I am new in the field of genetic algorithms, and I want to learn to use them in practice. How the genetic algorithm work and why it is applied ?
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2answers
48 views

Stereo matching using genetic algorithm

I have been reading a few papers (paper1, paper2) on stereo matching using genetic algorithms. I understand how genetic algorithms work in general and how stereo matching works, but I do not ...
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116 views

Genetic algorithm: How to crossover 2D permutation?

Each chromosome contains an array of genes, each gene contains a letter and a number, both letter and number can only exist once in each chromosome. ...
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1answer
60 views

Using NEAT, will the child of two parent genomes always have the same structure as the more fit parent?

I'm trying to implement the NEAT Algorithm using c#, based off of Kenneth O. Stanley's paper. On page 109 (12 in the pdf) it states "Matching genes are inherited randomly, whereas disjoint genes (...
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2answers
192 views

When should I use simulated annealing as opposed to a genetic algorithm?

What kind of problems is simulated annealing better suited for compared to genetic algorithms? From my experience, genetic algorithms seem to perform better than simulated annealing for most problems....
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1answer
97 views

Genetic Algorithm vs Particle Swarm Optimization

Which one gives better optimization results? Genetic Algorithm or Particle Swarm Optimization? Can I use them for online tuning problems? Thanks in advance!
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2answers
77 views

why we apply mutation after getting offspring in genetic algorithm?

When we get the offspring and fitness so why we prefer to apply mutation in genetic algorithm?
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1answer
386 views

Scrabble game using machine learning

I've been thinking if machine learning can be used to play the game Scrabble. My knowledge is limited in the ML field, thus I've seeking some pointers :) I want to know how could I possibly build a ...
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1answer
682 views

How to calculate fitness function of 8-queens problem?

In evolutionary computation and in particular in the context of genetic algorithms, there is a stochastic operation called "fitness function". The better a state, the greater the value of the fitness ...
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3answers
572 views

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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1answer
58 views

How can a genetic algorithm adapt and get better in a changing environment?

I've just started studying genetic algorithms and I'm not able to understand why a genetic algorithm can improve if, at each learning, the 'world' that the population encounters change. For example, ...
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1answer
110 views

Selection methods in genetic algorithms

In genetic algorithm, there are different steps. One of those steps is selection of chromosomes for reproduction (Evolution). In this step there are different methods are used for selection of ...
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4answers
2k views

What is the difference between “mutation” and “crossover”?

In the context of evolutionary computation, in particular genetic algorithms, there are two stochastic operations "mutation" and "crossover". What are the differences between them?
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4answers
198 views

What kind of algorithm can invent?

Set aside networks, image classification, gradients, and the strength of intelligence for a moment and consider the world before people lit fires. Fires were started periodically just as they are now,...
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3answers
117 views

Do genetic algorithm and neural networks really think?

I'm aware of those AI programmes which can play games and neural networks which can identify pictures. But are they really thinking. Do they think like humans? Do they have consciousness? Or are they ...
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1answer
72 views

How to use Genetic Algorithm for varying lengths of solutions

Until now, I always thought that Genetic Algorithm can be used for problems of which the solution space can be encoded (modeled) as a chromosome of a specific length. However, some people claim that ...
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4answers
228 views

Spontaneous emergence of replicators in Artificial Life

One of the corner stones of The Selfish Gene (Dawkins) is the spontaneous emergence of replicators, i.e. molecules capable of replicating themselves. Has this been modeled in silico in open-ended ...
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1answer
182 views

Fitness function in genetic algorithm based on an interval

I am writing an app, where when a ball is shot from a canon it is supposed to land in a hole which is on a given distance. The ball is supposed to land between the distance of the begining of the hole ...
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3answers
233 views

Do intelligent systems make mistakes?

The intelligence of the human brain is said to be a strong factor leading to human survival. The human brain functions as overseer for many functions the organism requires. Like that, the most ...
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2answers
177 views

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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97 views

Learning Genetic algorithm for beginners

What is the best and easiest programming language to learn to implement Genetic algorithms? C++ or Python or any other?
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712 views

NEAT + Keras : reproducibility problem (World Models implementation)

I'm trying to apply the World Models architecture to the Sonic game (using the gym-retro library). My problem concerns the evolutionnary algorithm part that I use as the controller (worldmodels = ...
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1answer
215 views

How to optimize a function using a genetic algorithm?

I recently learned about Genetic algorithms and I solved the 8 queens problem using a genetic algorithm but I don't know how to optimize any functions using a genetic algorithm. I want a guide on how ...