Questions tagged [genetic-algorithms]

Questions on genetic algorithms, a family of evolutionary search heuristics. Genetic Algorithms are adaptive heuristic search algorithms based on the evolutionary ideas of natural selection and generics.

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Algorithm for creating an esports season schedule

I run an esports league for my Boys & Girls Club and others that want to participate. I'm trying to find a way to take information about when different teams are available to compete and use that ...
malsatori's user avatar
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GA/GP subtree crossover/mutation with all items present?

I have a genetic algorithm/programming problem where I need the best arrange of items in a binary tree. My question is the best way to mutate/cross this binary tree subject to constraints described ...
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Mutation probability on strongly varying chromosome length

I am working on a Genetic (or rather Memetic) Algorithm for optimizing a scheduling problem. I have n orders to schedule and the solution representation is an array of n integers that specify when an ...
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Solving graph coloring problem using genetic algorithms

I have some basic information about the graph coloring problem, which is an NP-Complete problem. I am very new to genetic algorithms, and I have faced a problem in which we have to solve the graph ...
Aylin Naebzadeh's user avatar
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EvoPathfinding - Stuck in local optimal

I am using a Genetic Algorithm framework to solve a path-finding problem. Specifically, given the following 32x32 maze: ...
ex1led's user avatar
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When should genetic algorithm's variation operators be specialized?

Genetic algorithm (GA) is general purpose metaheuristic often used in computational science, e.g. computational physics. However, some authors of computational physics software, e.g. [1], tend to ...
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How to plot the 'back-to-home-city-path' in TSA without repeating cities

I'm doing an implementation of the traveling salesman problem using genetic algorithms, but I can't get it: If we need to get the best route in a certain set of cities and then go back to the first ...
deniable_encryption's user avatar
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Genetic algorithm, transformation methods for the different objective function

I am using a genetic algorithm for the optimization of a thermodynamic cycle. The problem has no analytical solution and the solution space is computationally large. The question is the following: How ...
Panus Kladus's user avatar
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what does $(1 + (λ, λ))$ Genetic Algorithm mean?

I am discovering the topic of Genetic Algorithms, I read a bit about it on wikipedia and towardsdatascience. When I checked papers some papers, I found them using the notation "$(1 + (λ, λ))$&...
user206904's user avatar
4 votes
3 answers
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Genetic algorithms applied to topological orderings of a DAG

I need to solve an optimization problem, whose search space is all possible topological orderings of a DAG. There is a cost function associated with each ordering, which has no simple mathematical ...
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How to choose the initial temperature in the Boltzmann selection method?

Using the Boltzmann selection in genetic algorithms, the probability of visiting a point in optimization space $X_j$ is $p(X_j)=\frac{\exp\frac{-f(X_j)}{T}}{\sum_i \exp \frac{-f(X_i)}{T}}$ my question ...
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How to mix populations in multi population genetic algorithm?

I'm working on an optimization problem using genetic algorithm. To increase diversity of potential solutions I'm using multi-population approach: Instead of evolving one population I run 10 ...
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Solving Budgeted Maximum Coverage Problem using Greedy and Genetic Algorithm

I am trying to solve the Budgeted Maximum Coverage Problem. I have read and implemented the greedy and modified-greedy methods to solve it, as proposed by Khuller. Both are approximation algorithms. ...
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What is the meaning of phenotype could be represented by a number of different genotype

I found this sentence in multiple sources but I didnt understand in one thesis With a neutral representation each phenotype could be represented by a number of different genotype and in another ...
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For a set of points P, connected by weighted edges (distances) I need a path through all points while minimizing the travel on any edge longer than X

For a given set of coordinates (lat/lng) I need a path which will visit each coordinate only once. The path needs to be selected to minimize the number of times the haversine distance between two ...
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Travelling Salesman Problem: Distance between solutions

I'm designing a genetic algorithm to solve the travelling salesman problem. So far, I've gotten fairly good results. I'm now trying to improve on them by implementing some sort of diversification ...
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Dynamic Chromosome Length in Genetic Algorithms

My inquiry concerns the length of chromosomes employed in genetic algorithms (GA), and more broadly in other classes of evolutionary algorithms. The chromosome length is fixed throughout a GA's run. ...
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Function minimization as genetic algorithm stop condition

I have implemented genetic algorithm for a problem where I have an objective function which I need to minimize: Cost = ax + by + cz -> min The genetic ...
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Find Best N Points From a Graph

I've given a task and wonder if there are any better solutions. Inputs of task are: Distance of district pairs Population of pairs Goal is: Find n districts at which its population and districts ...
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Can you apply neural networks to design algorithms?

I’m kind of a newbie to neural networks (and CS in general) but I was wondering if there are any methods to apply them in such a way with the aim of producing algorithms that solve difficult math ...
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How to be sure of a good solution in hyperparameter optimization

I have a very expensive black-box function and at least 15 parameters that I want to explore (usually 5-6 at a time). So far I have tried Genetic Algorithms and Gaussian Process Surrogate Optimization ...
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Repair operator for evolutionary algorithm

I am working on a resource allocation problem using an SPEA 2 evolutionary algorithm. The problem involves decision variables where each variable has a different domain e.g. $E_i \le d_i$ where $E_i$ ...
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Duplicate dominating parents when using Deterministic Crowding in Genetic Algorithm

This is the pseudocode for using deterministic crowding: ...
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Find consensus trajectory of how a genetic algorithm solves an optimization

I have implemented a genetic algorithm to find the evolutionary outcomes of a biological scenario. I simulate the evolution (i.e. optimization) of five traits in my model. I ran my code 100 times and ...
Armin Dadras's user avatar
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Adding constraints in grammar for Grammatical Evolution

I'm trying to use Grammatical Evolution for creating trading strategies. Each sentence in the grammar when evaluated gives a weight matrix of size n x p . (n is the length of backtesting period and p ...
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Crossover method for 3D velocity vector genetic algorithm

I am trying to write a genetic algorithm which will learn how to throw a ball at a target. I have successfully implemented code that will graph the projectile motion of a sphere given an input ...
F J's user avatar
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The number of samples in SUS selection method

In stochastic Universal Sampling selection method of genetic algorithm, the number of selected samples is usually static. I wanted to know if it is possible to select the number of these samples ...
helen's user avatar
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Modifying the genetic algorithm

I have a multi-objective optimization problem (NP-complete). To solve it, I've decided to use the genetic algorithm. I have 3 "areas" of optimization, say parameters ...
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Is it true that for every genetic algorithm there exists a non-genetic algorithm that achieves the same results more efficiently?

And if it is not true, what are the problem classes or characteristics for which genetic algorithms are superior?
Bradley Thomas's user avatar
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Number of Times to Run an Evolutionary (Genetic) Algorithm

Evolutionary algorithms like genetic algorithms (GAs) are typically run multiple times and the outputted results are averaged across successive runs. However, in the case of long-runtime algorithms (...
compbiostats's user avatar
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The solution to my wind farm layout optimization using GA produces different results every time i run it. Is their a way to curb it? [closed]

Each layout consists of 1000 turbines or more and is ranked based on power produced. The way my algorithm works is:- ...
NARENDER REDDY's user avatar
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Genetic programming - tournament selection and elite

I am writing a thesis about automatic GUI testing. In order to find the fittest strategy I am using GP. I am using tournament selection to select chromosomes for the next generation and elitism to ...
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Is this a good way of generating diverse solutions to a problem using a genetic algorithm?

Imagine a complicated blackbox-esque system that has 20 inputs and 5 outputs. I have a set of criteria I am able to use to construct a fitness function. I run a genetic algorithm to deduce values ...
John Salter's user avatar
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How can I write a genetic programming algorithm, given that the Halting problem is unsolvable?

I am learning genetic programming and to practice I want to write a simple algorithm which evolves a program that solves a simple function (say, square root). I intend to represent programs as ...
olinarr's user avatar
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How to improve convergence to an equilibrium value, & damp oscillation?

I am developing a program which seeks strategies for the players A, B in any of a family of simple 2-player gambling-games. The program iterates, using a genetic algorithm to determine, from the ...
Rosie F's user avatar
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What kind of standard deviation must be used in optimization algorithms?

I would like to ask about the standard deviation of objective function value. There are two types of standard deviations: Population standard deviation Sample standard deviation In metaheuristic ...
kylo 's user avatar
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1 answer
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Genetic algorithm problem

This is a problem I found in an old exam in my school. I have to solve this Genetic Algorithm problem: N students $x_{1},..,x_{N}$ have answered a quiz of 10 questions (True or False questions) and ...
Ahmed Cheikh's user avatar
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Crossover topologically identical neural networks

I have recently learned about artificial neural networks (very interesting) and genetic algorithms (also very interesting). I have read some suggestions concerning how to crossover two parent neural ...
nc404's user avatar
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1 answer
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Shortest common supersequence with Genetic Algorithm

I'm trying to solve the shortest common supersequence with Genetic Algorithm. I found it a little bit hard to reduce the size of the chromosomes in each generation. I know that the maximum size of ...
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genetic algorithm with different categories of parameters

I am trying to use a genetic algorithm to solve a problem. However, I find it difficult to represent the chromosomes in my problem. In the problem, I have two categories of parameters, and each gene ...
Noon's user avatar
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Generate a random tree population

Given a unbalanced k-ary tree base (with internal nodes that represent operators and leafs representing values) from the space of all unbalanced k-ary trees ...
Lorenz's user avatar
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1 answer
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Choosing Fitness function for float output in Genetic algorithm

I have a NN that has ten outputs. The output values range between 0 and 1. The elements in the target array are all zeros except one element, which is "one". I am searching for a Fitness Function ...
Max Z's user avatar
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1 answer
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Evolutionary algorithm - is there a relation between minimum iterations and size of decision variables

I am solving an optimization problem using SPEA2, my problem has three cases with decision variables 25, 50 and 100 in each case. I want to ask if there is some relationship between the number of ...
user76646's user avatar
3 votes
1 answer
366 views

Difference between selecting a large pool of individuals for reproduction, and selecting two parents specifically

I have implemented a genetic algorithm for an optimization problem and I'm now trying to improve it to see if I can find better solutions or faster convergence. I am confused about the selection part ...
Nick Thissen's user avatar
1 vote
0 answers
145 views

Chromosome length in Genetic Algorithms

In order to find the appropriate length of chromosomes in GA programming, the author of this book states: Suppose six decimal places for the variables' values is desirable. It is clear that to ...
RezAm's user avatar
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1 answer
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Crossover operator in genetic algorithms in Neural Networks

I am developing a neural network that is trained using a genetic algorithm. The neural network is a multilayer perceptron using $\tanh$ as its activation function. Currently, the genotype of the ...
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How is Rank Selection better than Random selection and RWS?

I'm having a rough time understanding the Rank Selection method for Genetic Algorithms. Here is what I think it does: Tour1's Fitness: 0.87 Tour2's Fitness: 1.22 Tour3's Fitness: 1.03 Tour4's ...
Haytam's user avatar
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2 votes
1 answer
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Genetic algorithm - fit max circles inside box - what chromossomes?

I am using a genetic algorithm to fit the max number of circles into a box. Right now my cromossomes are both coordinates of the each circle. I am not sure how to crossover and mutate the x and y ...
scottbear's user avatar
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2 answers
166 views

Genetic Algorithm - Fit max circles inside box

I am using a genetic algorithm to find the best way to pack circles inside a box without each touching the others and filling as much space as possible. My doubt is if an individual from a generation ...
scottbear's user avatar
3 votes
0 answers
114 views

Are there are satisfying explanations for why genetic algorithms work?

The following commentator writes: Having studied this extensively back when they were called Genetic Algorithms, I would like to offer a few insights. One of the biggest reasons they fell out of ...
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