Abstract: |
To design practical black-box optimizers, one of the primary goals is to minimize the amount of work that must be done by the user while ensuring that a high-quality solution will be found quickly and reliably. This paper shows that probabilistic model-building genetic algorithms (PMBGAs) provide a great framework for designing practical and powerful black-box optimizers. The paper focuses on two algorithms that are among the most powerful PMBGAs: The Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA). |