Overview
- Collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces
- Introduction to evolution strategies and parameter control
- Presents heuristic extensions that allow optimization in constrained, multimodal and multi-objective solution spaces
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)
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Table of contents (8 chapters)
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Front Matter
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Foundations
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Front Matter
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Advanced Optimization
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Front Matter
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Learning
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Front Matter
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Back Matter
Keywords
About this book
Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods.
Authors and Affiliations
Bibliographic Information
Book Title: A Brief Introduction to Continuous Evolutionary Optimization
Authors: Oliver Kramer
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-319-03422-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s) 2014
Softcover ISBN: 978-3-319-03421-8Published: 18 December 2013
eBook ISBN: 978-3-319-03422-5Published: 04 December 2013
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: XI, 94
Number of Illustrations: 5 b/w illustrations, 24 illustrations in colour