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A Brief Introduction to Continuous Evolutionary Optimization

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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.

Availability
Inventory Code Barcode Call Number Location Status
1408000715EB0003379006.3823 Kra aCentral Library (Database Springer E-Book)Available
Detail Information
Series Title
SpringerBriefs in Applied Sciences and Technology
Call Number
006.3823 Kra a
Publisher
Switzerland : Springer Cham.,
Collation
xi, 94p.: Ill.
Language
English
ISBN/ISSN
978-3-319-03422-5
Classification
006.3823
Content Type
Ebook
Media Type
-
Carrier Type
online resource
Edition
1
Subject(s)
Evolutionary Computation
Computational Intelligence
Specific Detail Info
-
Statement of Responsibility
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No other version available

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