A fast hybrid genetic algorithm for the quadratic, assignment problem

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 wellknown 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.