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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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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 ...
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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 "$(1 + (λ, λ))$&...
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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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For classifier systems, how is the genetic algorithm run on delayed class?

I am reading Signals and Boundaries and came across Classifier Systems. In the book, John Holland seems to imply that the signals in the input, as they come from a live environment, don't have an ...
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2 votes
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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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2 answers
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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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1 answer
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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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2 votes
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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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2 votes
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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 ...
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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 ...
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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 ...
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1 answer
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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?
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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 (...
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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:- ...
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1 vote
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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3 votes
1 answer
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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 ...
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1 vote
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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 ...
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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 ...
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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 ...
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2 answers
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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 ...
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3 votes
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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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1 answer
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Using 2-opt Heuristic in a Genetic Algorithm for TSP

I read few papers while trying to find some better approachs to solve the TSP (Traveling salesman problem) as close to the optimal solution as possible. I implemented a Improved Greedy Crossover (...
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How to find the best parameters of a Genetic Algorithm applied to the TSP problem?

I have an assignement where I need to use a Genetic Algorithm to solve the TSP (Traveling Salesman Problem). I alrerady implemented a solution in C# but the problem is we're asked to use some kind of ...
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Unknown length of chromosone in genetic algorithm

I've read some about genetic algorithms and the general approach, but I haven't found anything about using it when the length of the solution is unknown. How would the generation of the initial ...
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Evolutionary Algorithm and Correlation

Note: Not sure with the tags. I'm quite new to this area of Computer Science. I was used to just developing software and applications and applying certain algorithms when necessary. However, I am ...
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1 vote
2 answers
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What is the point of selection step in a genetic algorithm?

I'm reading about genetic algorithms, and I'm not sure I understand the point of selection step.Let's say we have a population of size $N$.How many chromosomes should we select using any selection ...
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Optimization of coefficients by using genetic algorithm

I want to optimization the coefficient of FIR filter using genetic algorithm method. The main data structures in the Genetic Algorithm are: chromosomes (vector) objective function values fitness ...
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