Improving Genetic Algorithms by Search Space Reductions (with Applications to Flow Shop Scheduling)

Crossover operators that preserve common components can also preserve representation level constraints. Consequently, these constraints can be used to beneficially reduce the search space. For example, in flow shop scheduling problems with order-based objectives (e.g. tardiness costs and earliness costs), search space reductions have been implemented with precedence constraints. Experiments show that these (heuristically added) constraints can significantly improve the performance of Precedence Preserving Crossover--an operator which preserves common (order-based) schemata. Conversely, the performance of Uniform Order-Based Crossover (the best traditional sequencing operator) improves less--it is based on combination. Overall, the results suggest that conditions exist where Precedence Preserving Crossover should be the best performing genetic sequencing operator.

[1]  Stephen F. Smith,et al.  Experiments on Commonality in Sequencing Operators , 1998 .

[2]  Thomas E. Morton,et al.  Heuristic scheduling systems : with applications to production systems and project management , 1993 .

[3]  Keith E. Mathias,et al.  Sequence Scheduling With Genetic Algorithms , 1992 .

[4]  Nicholas J. Radcliffe,et al.  Forma Analysis and Random Respectful Recombination , 1991, ICGA.

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

[6]  L. Darrell Whitley,et al.  GENITOR II: a distributed genetic algorithm , 1990, J. Exp. Theor. Artif. Intell..

[7]  Lawrence Davis,et al.  Applying Adaptive Algorithms to Epistatic Domains , 1985, IJCAI.

[8]  Dirk Van Gucht,et al.  Incorporating Heuristic Information into Genetic Search , 1987, International Conference on Genetic Algorithms.

[9]  L. Darrell Whitley,et al.  Comparing heuristic search methods and genetic algorithms for warehouse scheduling , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[10]  L. Escudero.  An inexact algorithm for the sequential ordering problem , 1988 .

[11]  Norbert Ascheuer,et al.  Hamiltonian path problems in the on-line optimization of flexible manufacturing systems , 1996 .

[12]  L. Darrell Whitley,et al.  Comparing Heuristic, Evolutionary and Local Search Approaches to Scheduling , 1996, AIPS.

[13]  Stephen F. Smith,et al.  Putting the "Genetics" Back into Genetic Algorithms (Reconsidering the Role of Crossover in Hybrid Operators) , 1998, FOGA.

[14]  John J. Grefenstette,et al.  Genetic Algorithms for the Traveling Salesman Problem , 1985, ICGA.

[15]  Keith E. Mathias,et al.  Convergence Controlled Variation , 1996, FOGA.

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

[17]  Christian Bierwirth,et al.  On Permutation Representations for Scheduling Problems , 1996, PPSN.

[18]  L. Darrell Whitley,et al.  A Comparison of Genetic Sequencing Operators , 1991, ICGA.

[19]  N. Hirabayashi,et al.  A decomposition scheduling method for operating flexible manufacturing systems , 1994 .

[20]  G. Rand.  Sequencing and Scheduling: An Introduction to the Mathematics of the Job-Shop , 1982 .

[21]  William L. Maxwell,et al.  Theory of scheduling , 1967 .

[22]  G. Syswerda,et al.  Schedule Optimization Using Genetic Algorithms , 1991 .

[23]  Shigenobu Kobayashi,et al.  Edge Assembly Crossover: A High-Power Genetic Algorithm for the Travelling Salesman Problem , 1997, ICGA.

[24]  G. Reinelt.  The traveling salesman: computational solutions for TSP applications , 1994 .