Vector quantization and signal compression
Vector quantization and signal compression
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A Branch & Cut Algorithm for the Asymmetric Traveling Salesman Problem with Precedence Constraints
Computational Optimization and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
New Genetic Local Search Operators for the Traveling Salesman Problem
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Voronoi Quantizied Crossover For Traveling Salesman Problem
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem
INFORMS Journal on Computing
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
New entropy-based measures of gene significance and epistasis
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A survey on chromosomal structures and operators for exploiting topological linkages of genes
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A heuristic manipulation technique for the sequential ordering problem
Computers and Operations Research
A survey on chromosomal structures and operators for exploiting topological linkages of genes
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A hybrid particle swarm optimization approach for the sequential ordering problem
Computers and Operations Research
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A hybrid genetic algorithm is proposed for the sequential ordering problem. It is known that the performance of a genetic algorithm depends on the survival environment and the reproducibility of building blocks. For decades, various chromosomal structures and crossover operators were proposed for the purpose. In this paper, we use Voronoi quantized crossover that adopts complete graph representation. It showed remarkable improvement in comparison with state-of-the-art genetic algorithms.