Improving EAX with restricted 2-opt
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Genetic operators for combinatorial optimization in TSP and microarray gene ordering
Applied Intelligence
Study of genetic algorithm with reinforcement learning to solve the TSP
Expert Systems with Applications: An International Journal
Multiagent optimization system for solving the traveling salesman problem (TSP)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A memetic algorithm for the generalized traveling salesman problem
Natural Computing: an international journal
Three-tier multi-agent approach for solving traveling salesman problem
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Application notes: memetic mission management
IEEE Computational Intelligence Magazine
Honey bees mating optimization algorithm for the Euclidean traveling salesman problem
Information Sciences: an International Journal
Complex Task Allocation in Mobile Surveillance Systems
Journal of Intelligent and Robotic Systems
Evolutionary discrete firefly algorithm for travelling salesman problem
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
New EAX crossover for large TSP instances
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
An improved multi-agent approach for solving large traveling salesman problem
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
Fast EAX algorithm considering population diversity for traveling salesman problems
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Combinatorial complexity problem reduction by the use of artificial vaccines
Expert Systems with Applications: An International Journal
High-Order sequence entropies for measuring population diversity in the traveling salesman problem
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
A Powerful Genetic Algorithm Using Edge Assembly Crossover for the Traveling Salesman Problem
INFORMS Journal on Computing
International Journal of Bio-Inspired Computation
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This work proposes an evolutionary algorithm, called the heterogeneous selection evolutionary algorithm (HeSEA), for solving large traveling salesman problems (TSP). The strengths and limitations of numerous well-known genetic operators are first analyzed, along with local search methods for TSPs from their solution qualities and mechanisms for preserving and adding edges. Based on this analysis, a new approach, HeSEA is proposed which integrates edge assembly crossover (EAX) and Lin-Kernighan (LK) local search, through family competition and heterogeneous pairing selection. This study demonstrates experimentally that EAX and LK can compensate for each other's disadvantages. Family competition and heterogeneous pairing selections are used to maintain the diversity of the population, which is especially useful for evolutionary algorithms in solving large TSPs. The proposed method was evaluated on 16 well-known TSPs in which the numbers of cities range from 318 to 13 509. Experimental results indicate that HeSEA performs well and is very competitive with other approaches. The proposed method can determine the optimum path when the number of cities is under 10 000 and the mean solution quality is within 0.0074% above the optimum for each test problem. These findings imply that the proposed method can find tours robustly with a fixed small population and a limited family competition length in reasonable time, when used to solve large TSPs.