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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22
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5answers
3k views

Why do low fitness individuals have a chance to survive to the next generation?

I am currently reading and watching about genetic algorithm and I find it very interesting (I haven't had the chance to study it while I was at the university). I understand that mutations are based ...
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2answers
639 views

Why are diploid (dominant/recessive) genes not used widely in genetic algorithms?

In most implementations of genetic algorithms, the focus is on crossover and mutation. But somehow, most of them leave out diploid (dominant/recessive) nature of genes. As far as my (limited) ...
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3answers
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Evolving artificial neural networks for solving NP problems

I've recently read a really interesting blog entry from Google Research Blog talking about neural network. Basically they use this neural networks for solving various problems like image recognition. ...
9
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2answers
193 views

Selection of parameters for genetic algorithm

How can one select the proper number of parameters for a genetic algorithm to model a given system? For example, say you want to optimize production of cars, and you have 1,000 measurements of hourly ...
7
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2answers
9k views

How to avoid getting stuck on local optimum, for genetic algorithms

I'm programming a genetic algorithm using grammatical evolution. My problem is that I reach local optimal values (premature convergence) and when that happens, I don't know what to do. I'm thinking ...
7
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3answers
5k views

How to analyze the performance of a genetic algorithm experimentally?

I have a genetic algorithm for an optimization problem. I plotted the running time of the algorithm on several runs on the same input and the same parameters (population size, generation size, ...
6
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1answer
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How to identify when to use Genetic Algorithm/Programming

I have been reading/studying on genetic algorithm/programming, and have implemented Traveling salesman problem. TSP is basically a permutation/combination problem, and I can understand how GA helps ...
6
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1answer
586 views

Should you use Genetic algorithm for an extremly large unstructured search space?

My search space is discrete and in the order of $10^{1360}$, with a probably very complex fitness surface. Is it hopeless to attempt to use GA for such a problem? One fitness evaluation could take 1-3 ...
6
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1answer
2k views

Classification of job shop scheduling problems

I'm writing a program (using genetic algorithms) that finds sort-of-optimal scheduling plan for a factory. The factory has several types of machines (say, ...
6
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1answer
2k views

Train a neural network to play tic tac toe using a genetic algorithm

I have an assignment for school, in which I have to build a neural network that will play tic tac toe, using genetic algorithms for training. The thing is that I am clueless on how to connect the two. ...
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2answers
1k views

Is Differential Evolution a genetic algorithm?

I am trying to classify the Differential Evolution algorithm according to the framework in the book: Introduction to Evolutionary Computing The authors classify the field of evolutionary ...
5
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2answers
4k views

Selection, crossover and mutation function choice in genetic algorithms

I have been developing a GA for one of the projects I'm working on. I have everything implemented with no problems and I really like how GAs work in general, it's a really cool and relatively new ...
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2answers
290 views

Does local optimization in a genetic algorithm decrease diversity?

I want to create a hybrid genetic algorithm for a project to solve really high dimensional problems (1000+) One of my ideas is to incorporate a local optimization method within GA so each individual ...
4
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2answers
647 views

Measuring and maintaining the diversity of individuals in Genetic Algorithm

While I was using the Genetic Algorithm to generate full correct Sudoku grids starting from a population of random grids, I occasionally face the problem of the process being stuck on a local maxima ...
4
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1answer
110 views

Are genetic algorithms an effective way to train neural networks?

It seems to me that genetic algorithms would be an ideal way to train neural networks so that they come to have the right weights, since they are especially good at escaping local minima, and ...
4
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1answer
230 views

Something I don't understand about Genetic Algorithms

I've had a bit of experience programming Neural networks but I am fairly new with genetic algorithms (I'm only 17). I have a major issue that I can't understand. If a child get's one chromatid from ...
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1answer
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Genetic Algorithm, Neural Network, Deep Learning, Machine Learning Similarities and Applications? [closed]

I am a computer engineering student and trying to get the idea behind all these Artificial Intelligence Concepts and applications. I know little theoretically about machine learning and some high ...
4
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2answers
167 views

Genetic algorithm: What is the expected number of strings that are explored?

My question concerns genetic algorithm searching along bit strings. Given: $N$ = population size $l$ = length of bit strings $p_c$ = probability that a single crossover occur (double crossover never ...
4
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2answers
220 views

Computer Music Composition

I've been looking into computer assisted music composition lately for my school project. While searching for literature I came across GenJam, an interactive jazz improvisation software which uses ...
4
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1answer
2k views

How to use Sigma Scaling in a genetic algorithm

I have a genetic algorithm in Java and I'm testing new types of selections. For my tests I'm using the De Jong Half Sphere, my fitness function is $x^2 + y^2$. The selection method used is Sigma ...
4
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2answers
394 views

Crossover method for genetic algorithm

I got an amount of numbers, they are shuffled and represent the individuals. For example (3,1,3,2,5,22,5) is one individual or (22,3,1,3,5,5,2). Mutation is done quite easy by permutation within an ...
4
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1answer
196 views

Lamarckian and Genetic algorithm

Yesterday I've done some research how to optimize genetic algorithm and I've encountered a very interesting theory that we can use Lamarckian theory (adaptive theory) to optimize the neural network. ...
4
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0answers
138 views

Stochasticity of Genetic Algorithm

I have a set of observations of real data, and a set of Random Variables I produce my self. The goal is to generated Random Variables with the same Distribution as the real data. To investigate the ...
3
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1answer
294 views

Genetic Algorithms - Why wouldn't we use the principle of Elitism?

In Genetic Algorithms, the idea of elitism is that we keep our best solution from the generation in our population regardless of what happens throughout the iteration. However, elitism seems to be ...
3
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2answers
79 views

Genetic Algorithms: Converge to best solution for one, few or many environments?

Let's say we take someone's garden (A) as the environment. We want a robot to pick up a series of eggs that chickens have laid in the garden, while covering as little ground as possible (those heavy ...
3
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1answer
5k views

How to stop genetic algorithm population converging to a single value

I've written a genetic algorithm (GA) that solves a 7-dimensional optimisation problem. All seven variables are floating point numbers. The problem is that the entire population seems to converge to ...
3
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2answers
543 views

What crossover operator should I use for arranging students in classrooms?

I am building a genetic algorithm to solve a "classroom map" problem. The goal is to place correctly students depending on several factors (shortsighted students, turbulent students...). Here, the ...
3
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1answer
1k views

Disadvantages to using simple step functions for activation in neural networks?

From what I have read, the main advantage to using tanh(x) or sigmoid(x) as an activation function for neural networks is that it is very easily differentiable. I am trying to implement a neural ...
3
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1answer
149 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 ...
3
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0answers
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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 ...
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0answers
1k views

Genetic algorithm crossover technique for solving graph colouring problem

I am trying to develop a genetic algorithm to solve a graph colouring problem. The problem is the standard graph colouring problem, given a graph $G = (V,E)$ where $V$ is the set of vertices $V=\{0 \...
2
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3answers
398 views

A genetic algorithm modified for a specific problem

I have a problem whose solution can be written as a binary string with a given length $N$, where $N$ is a given parameter. Standard GA works well on this problem. From runs of small values $N$, I ...
2
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2answers
161 views

Evolutionary algorithm - how to select the parents

I try to solve Physical Travelling Salesman Problem using evolutionary algorithm and I have diffucult to detemine how to choose the parent , on which we do the crossover. Assume I have popultion of ...
2
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2answers
30 views

Is selection in genetic algorithm considered to be a genetic operator?

It is listed as such in wiki, however my literature never refers to it as such. My gut tells me to follow papers and monographs on the matter, but my concern is that I will make an easily avoidable ...
2
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4answers
6k views

Standard Parameters for Genetic Algorithms

I'm currently writing my thesis, which uses genetic algorithms at some point. Now I need to define some parameters for the genetic algorithm I know that, because of the No Free Lunch Theorem there ...
2
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1answer
3k views

Genetic Algorithms - Tournament Selection

I know this should be a fairly simple concept but I have been Googling a lot and can't seem to find a definitive definition. If we had the following random population: ...
2
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1answer
62 views

Progressive discrete multifunction optimization

I have a set of functions $F$. The functions effectively take a set $S$ that is always a subset of a global set of all possible values $G$, where $|G|>1000$. (alternatively, they could take a $|G|$...
2
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1answer
866 views

NeuroEvolution: NEAT algorithm innovation numbers

I have been reading up on the NeuronEvolution of Augmented Topologies and there's this little thing that's been bothering me. While reading Kenneth Stanley's Paper on NEAT I came on this figure here: ...
2
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1answer
55 views

Measuring Fitness in Genetic Programming Functionology

I recently begun working on a genetic algorithm to solve for patterns in data. More specifically, it solves for functions given a sample of ins and outs. So a possible solution would be X+2 = Y. The ...
2
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1answer
80 views

Can the reproduction function of an evolutionary algorithm consider gene strength?

I am trying to solve the Zen Garden puzzle using an evolutionary algorithm. My question relates to evolutionary algorithms in general. Bit about evolutionary algorithms: An evolutionary algorithm is ...
2
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1answer
110 views

Mutations as a crossover by product

Let's say I'm writing a GA to find an optimal path to travel from point $A$ to point $B$. Genotypes are a list of directions (north, south, east, west) to follow. So a genotype "NENWEE" will move ...
2
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1answer
89 views

Fitness functions for low-dimensional parts of cooperative coevolution algorithms

In cooperative coevolution algorithms, a high dimensional vector is broken into smaller vectors, each of which is optimized separately using EAs for fewer dimensions and then recombined. What is the ...
2
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1answer
809 views

Genetic algorithm fitness function [closed]

I'm trying to write some little code (POC for the selection/mutation operators) that uses a genetic algorithm to solve a global maximum for a function. ...
2
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1answer
180 views

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 ...
2
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1answer
512 views

Are genetic algorithms special instances of random search done in an unexpectedly short run-time? [closed]

I was wondering since randomness is embedded in genetic algorithms at almost every level, is there a really fine line between genetic algorithms and pure random search? Ever since I finished my ...
2
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1answer
2k views

TSP genetic algorithm: what mutation function for adjacency representation?

When implementing TSP GA I decided for adjacency representation (i.e. $j$ value in $i$-th index means that node $j$ goes right after node $i$), as it enables interesting heuristical crossover ...
2
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1answer
146 views

Disruption in the context of Genetic Algorithms

I'm trying to do a homework problem which references "disruption" in Genetic Algorithms. True or false? For 1-point and 2-point crossover, the schemata which have bits that are close ...
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2answers
806 views

Why are syntax trees used in genetic programming?

Reading a course on genetic programming, the first chapter describes the syntax tree as the basic representation of programs in genetic programming. What are the reasons leading to the choice of a ...
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2answers
1k views

Genetic Algorithm Minimum Population Size

Is there a minimum limit to a pool (population) size when using the genetic algorithm to solve an optimization problem? For example a population of size 2.
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2answers
70 views

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 ...