The efficiency of hybrid mutation genetic algorithm for the travelling salesman problem

Authors:
K Katayama;H Sakamoto;H Narihisa
Affiliations:
Graduate School of Engineering Okayama University of Science-Ridai-cho 1-1, Okayama 700-0005, Japan;Graduate School of Engineering Okayama University of Science-Ridai-cho 1-1, Okayama 700-0005, Japan;Faculty of Engineering Okayama University of Science Ridai-cho 1-1, Okayama 700-0005, Japan
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2000

Citing 1
Cited 2

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)

Analysis of neighborhood generation and move selection strategies on the performance of tabu search

ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
A hybrid genetic algorithm and application to the crosstalk aware track assignment problem

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III

Quantified Score

Hi-index	0.98

Visualization

Abstract

In this paper, we present an efficient genetic algorithm (GA) for solving the travelling salesman problem (TSP) as a combinatorial optimization problem. In our computational model, we propose a complete subtour exchange crossover that does not break as some good subtours as possible, because the good subtours are worth preserving for descendants. Generally speaking, global search GA is considered to be better approaches than local searches. However, it is necessary to strengthen the ability of local search as well as global ones in order to increase a GA total efficiency. In this study, our GA applies a stochastic hill climbing procedure in the mutation process of the GA. Experimental results showed that the GA leads good convergence as high as 99 percent even for 500 cities TSP.