Adaptive Operator Selection for Optimization

Abstract : Evolutionary Algorithms have demonstrated their ability to address a wide range of optimization problems; but their performance relies on tuning a few parameters, depending on the problem at hand. During this thesis work, we have focused on the development of tools to automatically set some of these parameters, using machine learning techniques. More specifically, we have worked on the following sub-problem: given a number of available variation operators, it consists in selecting which is the best operator to be applied at each moment of the search, based on how the operators have performed up to the given time instant of the current search/optimization process. This approach is applied online, i.e., while solving the problem, using only the history of the current run to evaluate and decide between the operators; this paradigm is commonly referred to as Adaptive Operator Selection (AOS). To do AOS, we need two components: a Credit Assignment scheme, that defines how to reward the operators based on their impact in the search process, and an Operator Selection mechanism that, based on the rewards received, decides which is the best operator to be applied next. The contributions of this thesis, in summary, lie in the proposal and analysis of schemes to solve the AOS problem based on the Multi-Armed Bandit (MAB) paradigm; we have proposed different techniques, in order to enable a MAB algorithm to efficiently cope with the dynamics of evolution and with the very different characteristics of the problems to be tackled. The latest one, referred to as AUC-MAB, is able to efficiently control the application of operators, while being robust with respect to its own parameters.
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Álvaro Fialho. Adaptive Operator Selection for Optimization. Computer Science [cs]. Université Paris Sud - Paris XI, 2010. English. ⟨tel-00578431⟩

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