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Abstract
A novel idea of performing evolutionary computations for solving highly-dimensional multi-objective optimization (MOO) problems is proposed. The information about individual genders is applied. This information is drawn out of the fitness of individuals and applied during the parental crossover in the evolutionary multi-objective optimization (EMO) processes. The paper introduces the principles of the genetic-gender approach (GGA) and illustrates its performance by means of examples of multi-objective optimization tasks.
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- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Published in:
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Advances in Intelligent Systems and Computing
no. AISC 230,
pages 161 - 174,
ISSN: 2194-5357 - Title of issue:
- Intelligent Systems in Technical and Medical Diagnostics strony 161 - 174
- Language:
- English
- Publication year:
- 2014
- Bibliographic description:
- Kowalczuk Z., Białaszewski T.: Gender approach to multi-objective optimization of detection systems by pre-selection of criteria// Intelligent Systems in Technical and Medical Diagnostics/ ed. J.Korbicz, M.Kowal : Springer-Verlag Berlin Heidelberg, 2014, s.161-174
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-642-39881-0_13
- Verified by:
- Gdańsk University of Technology
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