Open AccessJournal Article
Two Individual Genetic Algorithm
Younis R. Elhaddad,Aiman Gannous +1 more
TL;DR: According to the results, the simple Genetic Algorithms with Multi-crossovers is much better than starting with population of 100 individuals and using only one type crossover (order crossover OX).
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Abstract: The particular interests of this paper is to explore if the simple Genetic Algorithms (GA) starts with population of only two individuals and applying different crossover technique over these parents to produced 104 children, each one has different attributes inherited from their parents; is better than starting with population of 100 individuals; and using only one type crossover (order crossover OX). For this reason we implement GA with 52 different crossover techniques; each one produce two children; which means 104 different children will be produced and this may discover more search space, also we implement classic GA with order crossover and many experiments were done over 3 Travel Salesman Problem (TSP) to find out which method is better, and according to the results we can say that GA with Multi-crossovers is much better. Keywords—Artificial Intelligence; Genetic Algorithm; order crossover; Travel Salesman Problem
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References
•Journal Article
An overview of genetic algorithms: Part 1, fundamentals
TL;DR: Genetic Algorithms (GAs) are adaptive methods which may be used to solve search and optimisation problems based on the genetic processes of biological organisms, which simulate those processes in natural populations which are essential to evolution.
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
TL;DR: This paper presents crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with different representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation.