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Abstract
Paper presents a computational optimization study using a genetic gender approach for solving multi-objective optimization problems of detection observers. In this methodology the information about an individual gender of all the considered solutions is applied for the purpose of making distinction between different groups of objectives. This information is drawn out of the fitness of individuals and applied during a current parental crossover in the performed evolutionary multi-objective optimization (EMO) processes.
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- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Title of issue:
- Advanced and Intelligent Computations in Diagnosis and Control strony 317 - 329
- ISSN:
- 2194-5357
- Language:
- English
- Publication year:
- 2016
- Bibliographic description:
- Białaszewski T., Kowalczuk Z.: Solving highly-dimensional multi-objective optimization problems by means of genetic gender// Advanced and Intelligent Computations in Diagnosis and Control/ ed. Z. Kowalczuk Cham – Heidelberg – New York – Dordrecht – London : Springer IP Switzerland, 2016, s.317-329
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-319-23180-8_23
- Verified by:
- Gdańsk University of Technology
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