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 in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Chained Lin-Kernighan for Large Traveling Salesman Problems
INFORMS Journal on Computing
iBOA: the incremental bayesian optimization algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
cAS: ant colony optimization with cunning ants
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Extended artificial chromosomes genetic algorithm for permutation flowshop scheduling problems
Computers and Industrial Engineering
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In previous study, we have proposed EHBSA within the EDA framework for permutation domains, and showed better performance than traditional GAs. The important feature of EHBSA is to use partial solutions from previous generations. In this paper, we analyze the effectiveness of using partial solutions using a wide range of problem sizes, without local search, and incorporating two types of local search. One of the most important finding in this paper is that we were able to confirm that using partial solutions is effective for all cases in which we use no local search, 3-OPT local search, and Lin-Kernighan (LK) local search. Future work for this research is also discussed.