A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
Artificial Intelligence Review
New Operators of Genetic Algorithms for Traveling Salesman Problem
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Applying adaptive algorithms to epistatic domains
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
A novel set-based particle swarm optimization method for discrete optimization problems
IEEE Transactions on Evolutionary Computation
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This paper presents a Strategy adaptive Genetic Algorithm to address a wide range of sequencing discrete optimization problems. As for the performance analysis, we have applied our algorithm on the Travelling Salesman Problem(TSP).Here we present an innovative crossover scheme which selects a crossover strategy from a consortium of three such crossover strategies, the choice being decided partly by the ability of the strategy to produce fitter off springs and partly by chance. We have maintained an account of each such strategy in producing fit off springs by adopting a model similar to The Ant Colony Optimization. We also propose a new variant of the Order Crossover which retains some of the best edges during the inheritance process. Along with conventional mutation methods we have developed a greedy inversion mutation scheme which is incorporated only if the operation leads to a more economical traversal. This algorithm provides better results compared to other heuristics, which is evident from the experimental results and their comparisons with those obtained using other algorithms.