Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
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)
Memetic algorithms: a short introduction
New ideas in optimization
Computational Optimization and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
A Taxonomy of Evolutionary Algorithms in Combinatorial Optimization
Journal of Heuristics
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
FANT: Fast ant system
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
Centric selection: a way to tune the exploration/exploitation trade-off
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Polynomial selection scheme with dynamic parameter estimation in cellular genetic algorithm
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A Tabu Search Approach for the NMR Protein Structure-Based Assignment Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Genetic algorithms (GAs) have recently become very popular by solving combinatorial optimization problems. In this paper, we propose an extension of the hybrid genetic algorithm for the well-known combinatorial optimization problem, the quadratic assignment problem (QAP). This extension is based on the "fast hybrid genetic algorithm" concept. An enhanced tabu search is used in the role of the fast local improvement of solutions, whereas a robust reconstruction (mutation) strategy is responsible for maintaining a high degree of the diversity within the population. We tested our algorithm on the instances from the QAP instance library QAPLIB. The results demonstrate promising performance of the proposed algorithm.