Adaptive Control of Genetic Parameters for Dynamic Combinatorial Problems

The idea of using diversity to guide evolutionary algorithms is gaining interest. However, it is mainly used in static problems or in dynamic continuous optimization problems. In this paper, we investigate the idea on dynamic combinatorial problems.

[1]  Gerhard Reinelt,et al.  TSPLIB - A Traveling Salesman Problem Library , 1991, INFORMS J. Comput..

[2]  Graham Kendall,et al.  Diversity in genetic programming: an analysis of measures and correlation with fitness , 2004, IEEE Transactions on Evolutionary Computation.

[3]  Rasmus K. Ursem,et al.  Diversity-Guided Evolutionary Algorithms , 2002, PPSN.

[4]  John J. Grefenstette,et al.  Evolvability in dynamic fitness landscapes: a genetic algorithm approach , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[5]  Kenneth Sörensen,et al.  MA mid PM: memetic algorithms with population management , 2006, Comput. Oper. Res..

[6]  Kenny Q. Zhu,et al.  A diversity-controlling adaptive genetic algorithm for the vehicle routing problem with time windows , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[7]  Mitsuo Gen,et al.  Genetic Algorithms , 1999, Wiley Encyclopedia of Computer Science and Engineering.

[8]  James Smith,et al.  A tutorial for competent memetic algorithms: model, taxonomy, and design issues , 2005, IEEE Transactions on Evolutionary Computation.

[9]  A. Tamhane,et al.  Multiple Comparison Procedures , 1989 .

[10]  Paul H. Calamai,et al.  Generalized benchmark generation for dynamic combinatorial problems , 2005, GECCO '05.

[11]  A. Tamhane,et al.  Multiple Comparison Procedures , 2009 .

[12]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[13]  Hartmut Schmeck,et al.  An Ant Colony Optimization approach to dynamic TSP , 2001 .

[14]  Kenny Q. Zhu,et al.  Population Diversity in Permutation-Based Genetic Algorithm , 2004, ECML.

[15]  Peter Merz,et al.  Advanced Fitness Landscape Analysis and the Performance of Memetic Algorithms , 2004, Evolutionary Computation.

[16]  Colin R. Reeves,et al.  Genetic Algorithms: Principles and Perspectives: A Guide to Ga Theory , 2002 .

[17]  A. Griffiths Introduction to Genetic Analysis , 1976 .

[18]  C. J. Eyckelhof,et al.  Ant Systems for a Dynamic TSP , 2002, Ant Algorithms.

[19]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[20]  Karsten Weicker,et al.  Performance Measures for Dynamic Environments , 2002, PPSN.

[21]  Darrell Whitley,et al.  The Travelling Salesman and Sequence Scheduling: Quality Solutions using Genetic Edge Recombination , 1990 .

[22]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[23]  B. Freisleben,et al.  Genetic local search for the TSP: new results , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).