Using Edge Histogram Models to solve Flow shop Scheduling Problems with Probabilistic Model-Building Genetic Algorithms

In evolutionary algorithms based on probabilistic modeling, the offspring population is generated according to the estimated probability density model of the parent instead of using recombination and mutation operators. In this chapter, we have proposed a probabilistic model-building genetic algorithms (PMBGAs) for solving flow shop scheduling problems using edge histogram based sampling algorithms (EHBSAs). The effectiveness of introducing the tag node (TN) in a string representation is also discussed.

[1]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[2]  D. J. Smith,et al.  A Study of Permutation Crossover Operators on the Traveling Salesman Problem , 1987, ICGA.

[3]  Pedro Larrañaga,et al.  Solving the Traveling Salesman Problem with EDAs , 2002, Estimation of Distribution Algorithms.

[4]  Louise Travé-Massuyès,et al.  Telephone Network Traffic Overloading Diagnosis and Evolutionary Computation Techniques , 1997, Artificial Evolution.

[5]  Shigeyoshi Tsutsui,et al.  Probabilistic Model-Building Genetic Algorithms in Permutation Representation Domain Using Edge Histogram , 2002, PPSN.

[6]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[7]  Michèle Sebag,et al.  Extending Population-Based Incremental Learning to Continuous Search Spaces , 1998, PPSN.

[8]  Paul A. Viola,et al.  MIMIC: Finding Optima by Estimating Probability Densities , 1996, NIPS.

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

[10]  Darrell Whitley,et al.  Scheduling problems and traveling salesman: the genetic edge recombination , 1989 .

[11]  Shumeet Baluja,et al.  Using Optimal Dependency-Trees for Combinational Optimization , 1997, ICML.

[12]  Mitsunori Miki,et al.  SOLVING FLOW SHOP SCHEDULING PROBLEMS WITH PROBABILISTIC MODEL-BUILDING GENETIC ALGORITHMS USING EDGE HISTOGRAMS , 2002 .

[13]  P. Bosman,et al.  Continuous iterated density estimation evolutionary algorithms within the IDEA framework , 2000 .

[14]  David E. Goldberg,et al.  The compact genetic algorithm , 1999, IEEE Trans. Evol. Comput..

[15]  L. Darrell Whitley,et al.  Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator , 1989, International Conference on Genetic Algorithms.

[16]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[17]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[18]  David E. Goldberg,et al.  Linkage Problem, Distribution Estimation, and Bayesian Networks , 2000, Evolutionary Computation.

[19]  S. Baluja,et al.  Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space , 1997 .

[20]  G. Harik.  Linkage Learning via Probabilistic Modeling in the ECGA , 1999 .

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

[22]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[23]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[24]  P. Bosman,et al.  An algorithmic framework for density estimation based evolutionary algorithms , 1999 .