MA mid PM: memetic algorithms with population management

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.

[1]  Fred Glover,et al.  Critical Event Tabu Search for Multidimensional Knapsack Problems , 1996 .

[2]  Kenneth Sörensen,et al.  Distance measures based on the edit distance for permutation-type representations , 2007, J. Heuristics.

[3]  Chris N. Potts,et al.  Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem , 1998, INFORMS J. Comput..

[4]  S. Ronald Distance functions for order-based encodings , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[5]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[6]  Stefan Voß,et al.  Dynamic tabu list management using the reverse elimination method , 1993, Ann. Oper. Res..

[7]  S. Ronald,et al.  More distance functions for order-based encodings , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[8]  Esko Ukkonen,et al.  Finding Approximate Patterns in Strings , 1985, J. Algorithms.

[9]  Jacques A. Ferland,et al.  Scheduling using tabu search methods with intensification and diversification , 2001, Comput. Oper. Res..

[10]  Philippe Baptiste,et al.  A Branch-and-Bound procedure to minimize total tardiness on one machine with arbitrary release dates , 2004, Eur. J. Oper. Res..

[11]  Colin R. Reeves,et al.  Using Genetic Algorithms with Small Populations , 1993, ICGA.

[12]  Michael L. Mauldin,et al.  Maintaining Diversity in Genetic Search , 1984, AAAI.

[13]  Samir W. Mahfoud Crowding and Preselection Revisited , 1992, PPSN.

[14]  Rafael Martí,et al.  Intensification and diversification with elite tabu search solutions for the linear ordering problem , 1999, Comput. Oper. Res..

[15]  Colin R. Reeves,et al.  Genetic Algorithms for the Operations Researcher , 1997, INFORMS J. Comput..

[16]  Chris N. Potts,et al.  An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem , 2002, INFORMS J. Comput..

[17]  E. Lawler A “Pseudopolynomial” Algorithm for Sequencing Jobs to Minimize Total Tardiness , 1977 .

[18]  Michael J. Shaw,et al.  Genetic algorithms with dynamic niche sharing for multimodal function optimization , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[20]  Fred W. Glover,et al.  A Template for Scatter Search and Path Relinking , 1997, Artificial Evolution.

[21]  John E. Beasley,et al.  A Genetic Algorithm for the Multidimensional Knapsack Problem , 1998, J. Heuristics.

[22]  K. Sörensen,et al.  A framework for robust and flexible optimisation using metaheuristics with applications in supply chain design , 2003 .

[23]  Gerard Gaalman,et al.  Proceedings of the workshop on production planning and control , 1996 .

[24]  Michael J. Fischer,et al.  The String-to-String Correction Problem , 1974, JACM.

[25]  Alain Hertz,et al.  Guidelines for the use of meta-heuristics in combinatorial optimization , 2003, Eur. J. Oper. Res..

[26]  Rafael Martí,et al.  Context-Independent Scatter and Tabu Search for Permutation Problems , 2005, INFORMS J. Comput..

[27]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[28]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .