Two Individual Genetic Algorithm
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079510Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1327
 Artificial Intelligence: A Modern Approach. Russell, S and Norvig, P. New Jersey : Prentice Hall, 1995.
 http://www.iwr.uniheidelberg.de/groups/comopt/software/TSPLIB95/.H eidelberg University
 Beasley, D, Bull, D R and Martin, R. An Overview of Genetic Algorithms :. Part 1, Fundamentals. Norwegian University of Science and Technology.
 P. Larranaga, C.M.H. Kuijpers, R.H. Murga, I. Inza and S. Dizdarevic. Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators