Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016; Lisbon; Lisbon
Год издания: 2016
Ключевые слова: Ensemble methods, genetic algorithms, Multimodal optimization, Selective hyper-heuristic
Аннотация: Many problems of design and decision making support can be stated as optimization problems. For real-world problems, sometimes it is necessary to obtain many alternative solutions to the problem. In this case multimodal approach can be used. The goal of multimodal optimization (MMO) is to find all optima (global and local) or a representative subset of all optima. In recent years many efficient nature-inspired techniques have been proposed for real-valued MMO problems. At the same time, real-world design and decision making support problems may contain variables of many different types, including integer, rank, binary and others. In this case, the weakest representation (namely binary representation) is used. Unfortunately, there is a lack of efficient approaches for problems with binary representation. In this study, a novel approach based on a selective hyper-heuristic in a form of ensemble for designing multi-strategy genetic algorithm is proposed. The approach controls the interactions of many search techniques (different genetic algorithms for MMO) and leads to the self-configuring solving of problems with a priori unknown structure. The results of numerical experiments for benchmark problems from the CEC competition on MMO and for some real-world problems are presented and discussed.
Журнал: ICINCO 2016 - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics
Выпуск журнала: 1
Номера страниц: 160-167
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