Questions on genetic algorithms, a family of evolutionary search heuristics.

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Sigma scaling problems [migrated]

I'm developing an evolutionary genetic algorithm for which I am to use sigma scaling in order to scale the fitness evaluation of the individuals before selecting mates. Sigma scaling of fitness for ...
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What use does the predictor stage in a particle filter have?

Im a bit confused about the particle filter. I understand the generic particle filter algorithm but in some literature say particle filter has predictor stage which is not mentioned in the generic ...
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2answers
45 views

How do genetic algorithms work exactly?

I was looking at the basic genetic algorithm here http://www.ai-junkie.com/ga/intro/gat1.html But I have some questions about things I didn't get. To reiterate: You have a problem you want a ...
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1answer
130 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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3answers
742 views

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. ...
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1answer
45 views

Comparing different implementations of genetic algorithms

I am looking up some material for my thesis in CS (development of a module to integrate a genetic algorithm in a system developed by other students). My actual current task is to make a comparative ...
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141 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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1answer
175 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 ...
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1answer
49 views

A clarification on the taxonomy of Evolutionary Algorithms

A rather basic question but I am confused about the characterization of a certain local search method which I want to describe in the framework of EAs. In particular, consider an EA which in every ...
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126 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 ...
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1answer
39 views

Looking for an algorithm to generate an identicon/avatar from genome data

I am looking to develop an app that generates a single identicon image that summarizes the genome information in visual form. Identicons are essentially a visual hash of of data. usually string data ...
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1answer
188 views

Extended Compact Genetic Algorithm question

I have a question regarding the ECGA algorithm. This is what I'm currently studying. If ECGA builds a model of the parent pool using the 'minimum description length' (MDL) measure, why cannot we ...
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1answer
74 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 ...
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2answers
455 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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1answer
468 views

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 ...
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2answers
171 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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1answer
41 views

The name for this genetic algorithm variant

What is the name for this variant of genetic algorithm. I'm sure I have read about this in Wikipedia, but now I could not find it: There in no thing as sequential population. The phenotypes lives and ...
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2answers
573 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 ...
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203 views

Improving MSE as fitness function for a genetic algorithm

I am implementing an autoencoder neural network in matlab, the weights of which are being optimised by a genetic algorithm. At the moment I am working on the first layer, trying to get an improved ...
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1answer
236 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 ...
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1answer
45 views

Bit distance and disruption [duplicate]

Earlier, I asked a question defining disruption in Genetic Algorithms. Given that definition, I'm still confused on how to answer the following question. True or false? For 1-point and 2-point ...
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1answer
53 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 ...
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1answer
99 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 ...
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0answers
21 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 ...
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5answers
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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
37 views

Programming a genetic algorithm with a non-fixed size

I am trying to write a genetic algorithm for a program. Most examples for genetic algorithms use something like this as the input: aaaaaaaaaa and ...
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1answer
2k 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 ...
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Does using diploid (dominant/recessive) genes in genetic algorithm offer any advantage? [duplicate]

I've been looking into diploid genetic algorithms for a while. Although, it seems like an implementation which includes diploid (dominant/recessive) genes is closer to the implementation that has ...
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1answer
226 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. ...
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1answer
221 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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2answers
93 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 ...
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2answers
128 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 ...
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2answers
117 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 ...
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2answers
584 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, ...
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3answers
209 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 ...
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142 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 ...
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1answer
604 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 ...
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1answer
34 views

Is there a crossover operator to cross sections of specific phenotype genes?

For example, I've a chromosome with 10 genes, the first 5 genes represent a specific property of phenotype and the last 5 genes represent another property of phenotype. So, I need a crossover ...
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1answer
163 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
748 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, ...
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4answers
1k 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 ...
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2answers
82 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 ...
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1answer
179 views

Evolutionary algorithm for the Physical Travelling Salesman Problem

I want to solve the Physical Travelling Salesman Problem using an evolutionary algorithm. The objective of the PTSP is to visit the maximum number of waypoints of the map in the minimum number of ...
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1answer
217 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 ...
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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 ...
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1answer
106 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. ...