Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Estimation of distribution algorithm based on hidden Markov models for combinatorial optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Combinatorial optimization EDA using hidden Markov models
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper describes and analyzes an estimation of distribution algorithm based on dependency tree models (dtEDA), which can explicitly encode probabilistic models for permutations. dtEDA is tested on deceptive ordering problems and a number of instances of the quadratic assignment problem. The performance of dtEDA is compared to that of the standard genetic algorithm with the partially matched crossover (PMX) and the linear order crossover (LOX). In the quadratic assignment problem, the robust tabu search is also included in the comparison.