Physical mapping of chromosomes: a combinatorial problem in molecular biology
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
The Traveling Salesrep Problem, Edge Assembly Crossover, and 2-opt
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Deterministic Multi-step Crossover Fusion: A Handy Crossover Composition for GAs
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Scheduling by Genetic Local Search with Multi-Step Crossover
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Solving traveling salesman problems by combining global and local search mechanisms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
An evolutionary algorithm for large traveling salesman problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
How autonomy oriented computing (AOC) tackles a computationally hard optimization problem
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Study of genetic algorithm with reinforcement learning to solve the TSP
Expert Systems with Applications: An International Journal
Solving 0-1 knapsack problems via a hybrid differential evolution
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
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Edge Assembly Crossover (EAX) is by far the most successful crossover operator in solving the traveling salesman problem (TSP) with Genetic Algorithms (GAs). Various improvements have been proposed for EAX in GA. However, some of the improvements have to make compromises between performance and solution quality. In this work, we have combined several improvements proposed in the past, including heterogeneous pair selection (HpS), iterative child generation (ICG), and 2-opt. We also incorporate 2-opt into EAX, and restricted the 2-opt local searches to sub-tours in the intermediates generated by EAX.Our proposed method can improve the performance of EAX with decreased number of generations, error rates, and computation time. The applications of conventional 2-opt and our restricted 2-opt concurrently have additive effect on the performance gain, and this performance improvement is more obvious in larger problems. The proposed method also enhanced the solution quality of EAX. The significances of the restricted 2-opt and the conventional 2-opt in EAX were analyzed and discussed.