Tagged Questions

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

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0
votes
1answer
50 views

Which type of randomized algorithm is best suited for web crawling?

I have decided to implement a web crawler for my CS major project. The project is focused towards adaptive search. I want the pages to be as user specific as possible and time efficiency is not much a ...
1
vote
0answers
49 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 ...
1
vote
0answers
29 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 ...
1
vote
1answer
39 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 ...
2
votes
1answer
48 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 ...
2
votes
1answer
77 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 ...
1
vote
0answers
18 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 ...
15
votes
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 ...
0
votes
1answer
25 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 ...
3
votes
1answer
352 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 ...
0
votes
0answers
26 views

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 ...
2
votes
1answer
88 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. ...
11
votes
1answer
86 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) ...
8
votes
2answers
43 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 ...
3
votes
2answers
90 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 ...
3
votes
2answers
97 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 ...
3
votes
2answers
218 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, ...
2
votes
3answers
118 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 ...
1
vote
0answers
69 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
votes
1answer
264 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 ...
0
votes
1answer
30 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 ...
4
votes
1answer
132 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 ...
3
votes
1answer
306 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, ...
1
vote
3answers
478 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
votes
1answer
66 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 ...
1
vote
1answer
132 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 ...
2
votes
1answer
120 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 ...
4
votes
0answers
51 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
votes
0answers
72 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. ...