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2017 | OriginalPaper | Buchkapitel

Hybrid Initialization in the Process of Evolutionary Learning

verfasst von : Krystian Łapa, Krzysztof Cpałka, Yoichi Hayashi

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

Population-based algorithms are an interesting tool for solving optimization problems. Their performance depends not only on their specification but also on methods used for initialization of initial population. In this paper a new hybridization approach of initialization methods is proposed. It is based on classification of initialization methods that allow various combination of the methods from each category. To test the proposed approach typical problems related to population-based algorithms were used.

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Vorheriges Kapitel Dynamic Difficulty Adjustment for Serious Game Using Modified Evolutionary Algorithm

Nächstes Kapitel A Tuning of a Fractional Order PID Controller with the Use of Particle Swarm Optimization Method

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Titel: Hybrid Initialization in the Process of Evolutionary Learning
verfasst von: Krystian Łapa
Krzysztof Cpałka
Yoichi Hayashi
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Soft Computing
Print ISBN: 978-3-319-59062-2

Electronic ISBN: 978-3-319-59063-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59063-9_34

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