SOLVING THE QUADRATIC ASSIGNMENT PROBLEM WITH CLUES FROM NATURE

This paper describes a new evolutionary approach to solving quadratic assignment problems. The proposed technique is based loosely on a clas s of search and optimization algorithms known as evolution strategies (ES). These methods are inspired by the mechanics of biological evolut ion and have been applied successfully to a variety of difficult probl ems, particularly in continuous optimization. The combinatorial varian t of ES presented here performs very well on the given test problems a s compared with the standard 2-Opt heuristic and recent results with s imulated annealing and TABU search. Extensions for practical applicati ons in factory layout are described.