2009Unpublished venueRequires access

New Crossover Operator of Genetic Algorithms for the TSP

Su Fanchen, Fuxi Zhu, Zhiyi Yin, Haitao Yao, Qingping Wang, Wenyong Dong

Open publisher page 15 citations

Abstract

This paper describes a novel crossover operator, Cut-blend crossover, for a genetic algorithm for the TSP. Cut-blend crossover may be the best in such kind crossovers that improve a tour using a sub-tour extracted from other tour or tours (PMX, OX et al.). The proposed operators are embedded in a new genetic algorithm, which extracts sub-tours from a pool consisting of former best tours and current population, to compare with other crossovers. The operator is evaluated on a number of well-known benchmarks, e.g. oliver30, eil76, ch130 and pcb442 in the TSPLIB. Experimental results show that the new crossover is superior to the conventional crossovers such as OX, ER, especially in problems of larger scale.

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What this paper is about

This paper describes a novel crossover operator, Cut-blend crossover, for a genetic algorithm for the TSP. Cut-blend crossover may be the best in such kind crossovers that improve a tour using a sub-tour extracted from other tour or tours (PMX, OX et al.). The proposed operators are embedded in a new genetic algorithm, which extracts sub-tours from a pool consisting of former best tours and current population, to compare with other crossovers. The operator is evaluated on a number of well-known benchmarks, e.g. oliver30, eil76, ch130 and pcb442 in the TSPLIB. Experimental results show that the new crossover is superior to the conventional crossovers such as OX, ER, especially in problems of larger scale.

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Available abstract

This paper describes a novel crossover operator, Cut-blend crossover, for a genetic algorithm for the TSP. Cut-blend crossover may be the best in such kind crossovers that improve a tour using a sub-tour extracted from other tour or tours (PMX, OX et al.). The proposed operators are embedded in a new genetic algorithm, which extracts sub-tours from a pool consisting of former best tours and current population, to compare with other crossovers. The operator is evaluated on a number of well-known benchmarks, e.g. oliver30, eil76, ch130 and pcb442 in the TSPLIB. Experimental results show that the new crossover is superior to the conventional crossovers such as OX, ER, especially in problems of larger scale.

Key concepts: Crossover, Operator (biology), Genetic algorithm, Travelling salesman problem, Algorithm, Population, Computer science, Mathematical optimization

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