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Design of gray-box evolutionary algorithms and applications

Abstract

Information about the structure of the problem is available in many optimization applications. However, this information is neglected by the vast majority of Evolutionary Algorithms (EAs), making optimization a black-box process. In contrast, gray-box optimization takes advantage of the knowledge of the interaction between decision variables to guide the search. Recently, new operators and gray-box type strategies have been developed, allowing to significantly improve the performance of EAs. This project aims to develop research involving efficient EAs in two main areas. From the point of view of designing new algorithms, the main objective is the development of recombination and perturbation operators that use the variable interaction graph to generate new solutions. From the application point of view, efficient EAs will be applied to problems involving Machine Learning in Medicine. (AU)

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