USING EDGE HISTOGRAM MODELS TO SOLVE FLOW SHOP SCHEDULING PROBLEMS WITH PROBABILISTIC MODEL-BUILDING GENETIC ALGORITHMS
Shigeyoshi Tsutsui, Mitsunori Miki · Advances in natural computation · 2004
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.