Novel Crossover Operator for Genetic Algorithm for Permutation Problems
Rakesh Kumar1, Girdhar Gopal2, Rajesh Kumar3
1Rakesh Kumar, DCSA, Kurukshetra University Kurukshetra, Haryana, India.
2Girdhar Gopal, DCSA, Kurukshetra University Kurukshetra, Haryana, India.
3Rajesh Kumar, DCSA, Kurukshetra University Kurukshetra, Haryana, India.
Manuscript received on April 05, 2013. | Revised Manuscript received on April 28, 2013. | Manuscript published on May 05, 2013. | PP: 252-258 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1486053213/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Simple Symmetric Traveling Salesman Problem (TSP) has a combinational nature. When there are 25 or more cities to visit, brute force search is not feasible. Instead, heuristic & probabilistic search methods are more reasonable for obtaining optimal solutions. In this paper, Genetic algorithm and crossover are researched and a novel crossover operator has been introduced by combining two existing crossover methods named PMX and OX crossover. The proposed operator is tested on 4 different inputs from TSPLIB provided by Heidelberg University and the result are compared with Partial Matched Crossover(PMX), Order Crossover(OX) and cyclic crossover(CX) and is found that proposed crossover has outperformed the rest in all the problems..
Keywords: Crossover, Genetic Algorithm, Traveling Salesman Problem (TSP).