MA|PM: memetic algorithms with population management

Authors:
Kenneth Sörensen;Marc Sevaux
Affiliations:
University of Antwerp, Faculty of Applied Economics, Prinsstraat 13, B-2000 Antwerp, Belgium;University of Valenciennes, CNRS, UMR 8530, LAMIH-SP, Le Mont Houy-Bat Jonas 2, F-59313 Valenciennes cedex 9, France
Venue:
Computers and Operations Research
Year:
2006

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Abstract

A new metaheuristic for (combinatorial) optimization is presented: memetic algorithms with population management or MA|PM. An MA|PM is a memetic algorithm, that combines local search and crossover operators, but its main distinguishing feature is the use of distance measures for population management. Population management strategies can be developed to dynamically control the diversity of a small population of high-quality individuals, thereby avoiding slow or premature convergence, and achieve excellent performance on hard combinatorial optimization problems. The new algorithm is tested on two problems: the multidimensional knapsack problem and the weighted tardiness single-machine scheduling problem. On both problems, population management is shown to be able to improve the performance of a similar memetic algorithm without population management.