A Brief Introduction to Continuous Evolutionary Optimization A Brief Introduction to Continuous Evolutionary Optimization

A Brief Introduction to Continuous Evolutionary Optimization

    • $39.99
    • $39.99

Publisher Description

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.

GENRE
Computers & Internet
RELEASED
2013
December 4
LANGUAGE
EN
English
LENGTH
105
Pages
PUBLISHER
Springer International Publishing
SELLER
Springer Nature B.V.
SIZE
2.3
MB

More Books by Oliver Kramer

Genetic Algorithm Essentials Genetic Algorithm Essentials
2017
Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy
2017
Data Analytics for Renewable Energy Integration Data Analytics for Renewable Energy Integration
2017
Machine Learning for Evolution Strategies Machine Learning for Evolution Strategies
2016
Computational Intelligence Computational Intelligence
2009
New Advances in Virtual Humans New Advances in Virtual Humans
2008