A genetic approach to the quadratic assignment problem
Computers and Operations Research - Special issue on genetic algorithms
P-Complete Approximation Problems
Journal of the ACM (JACM)
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
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
FANT: Fast ant system
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
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
A fast hybrid genetic algorithm for the quadratic assignment problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Compounded genetic algorithms for the quadratic assignment problem
Operations Research Letters
Optimization and Knowledge-Based Technologies
Informatica
Information Sciences: an International Journal
Enhancing the performance of hybrid genetic algorithms by differential improvement
Computers and Operations Research
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In this paper, an efficient hybrid genetic algorithm (HGA) and its variants for the well-known combinatorial optimization problem, the quadratic assignment problem (QAP) are discussed. In particular, we tested our algorithms on a special type of QAPs, the structured quadratic assignment problems. The results from the computational experiments on this class of problems demonstrate that HGAs allow to achieve near-optimal and (pseudo-)optimal solutions at very reasonable computation times. The obtained results also confirm that the hybrid genetic algorithms are among the most suitable heuristic approaches for this type of QAPs.