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
A genetic approach to the quadratic assignment problem
Computers and Operations Research - Special issue on genetic algorithms
Memetic algorithms: a short introduction
New ideas in optimization
Computational Optimization and Applications
A greedy genetic algorithm for the quadratic assignment problem
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
INFORMS Journal on Computing
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Adaptive memories for the Quadratic Assignment Problems
Adaptive memories for the Quadratic Assignment Problems
A New Genetic Algorithm for the Quadratic Assignment Problem
INFORMS Journal on Computing
A tabu search algorithm for the quadratic assignment problem
Computational Optimization and Applications
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
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Recently, genetic algorithms (GAs) and their hybrids have achieved great success in solving difficult combinatorial optimization problems. In this paper, the issues related to the performance of the genetic search in the context of the grey pattern problem (GPP) are discussed. The main attention is paid to the investigation of the solution recombination, i.e., crossover operators which play an important role by developing robust genetic algorithms. We implemented seven crossover operators within the hybrid genetic algorithm (HGA) framework, and carried out the computational experiments in order to test the influence of the recombination operators to the genetic search process. We examined the one point crossover, the uniform like crossover, the cycle crossover, the swap path crossover, and others. A so-called multiple parent crossover based on a special type of recombination of several solutions was tried, too. The results obtained from the experiments on the GPP test instances demonstrate promising efficiency of the swap path and multiple parent crossovers.