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
Local Optimization and the Traveling Salesman Problem
ICALP '90 Proceedings of the 17th International Colloquium on Automata, Languages and Programming
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
Exact Solutions to the Traveling Salesperson Problem by a Population-Based Evolutionary Algorithm
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows
Computers and Operations Research
Edge assembly crossover for the capacitated vehicle routing problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
New EAX crossover for large TSP instances
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A new genetic algorithm for the asymmetric traveling salesman problem
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
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This paper proposes an evolutionary algorithm (EA) that is applied to the traveling salesman problem (TSP). Existing approximation methods to address the TSP known to be state-of-the-art heuristics almost exclusively utilize Lin-Kernighan local search (LKLS) and its variants. We propose an EA that does not use LKLS, and demonstrate that it is comparable with these heuristics even though it does not use them. The proposed EA uses edge assembly crossover (EAX) that is known to be an efficient and effective crossover for solving TSPs. We first propose a modified EAX algorithm that can be executed more efficiently than the original, which is 2–7 times faster. We then propose a selection model that can efficiently maintain population diversity at negligible computational cost. The edge entropy measure is used as an indicator of population diversity. The proposed method called EAX-1AB(ENT) is applied to TSP benchmarks up to instances of 13509 cities. Experimental results reveal that EAX-1AB(ENT) with a population of 200 can almost always find optimal solutions effectively in most TSP benchmarks up to instances of 5915 cities. In the experiments, a previously proposed EAs using EAX can find an optimal solution of usa13509 with reasonable computational cost due to the fast EAX algorithm proposed in this paper. We also demonstrate that EAX-1AB(ENT) is comparable to well-known LKLS methods when relatively small populations such as 30 are used.