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Minimization of Systemic Risk for Directed Network Using Genetic Algorithm Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Two-Phase Strategy Managing Insensitivity in Global Optimization

Authors : Jakub Sawicki, Maciej Smołka, Marcin Łoś, Robert Schaefer, Piotr Faliszewski

Published in: Applications of Evolutionary Computation

Publisher: Springer International Publishing

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Abstract

Solving ill-posed continuous, global optimization problems remains challenging. For example, there are no well-established methods for handling objective insensitivity in the neighborhood of solutions, which appears in many important applications, e.g., in non-invasive tumor tissue diagnosis or geophysical exploration. The paper presents a complex metaheuristic that identifies regions of objective function’s insensitivity (plateaus). The strategy is composed of a multi-deme hierarchic memetic strategy coupled with random sample clustering, cluster integration, and special kind of multiwinner selection that allows to breed the demes and cover each plateau separately. We test the method on benchmarks with multiple non-convex plateaus and evaluate how well the plateaus are covered.

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previous chapter Lamarckian and Lifelong Memetic Search in Agent-Based Computing
next chapter Avenues for the Use of Cellular Automata in Image Segmentation
Footnotes
1
In the theory of elections it is often assumed that a rule can output several tied committees, and these ties have to somehow be broken. In our application it is far simpler to assume that tie-breaking already happened within the rule and we get a unique outcome.
 
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Metadata
Title
Two-Phase Strategy Managing Insensitivity in Global Optimization
Authors
Jakub Sawicki
Maciej Smołka
Marcin Łoś
Robert Schaefer
Piotr Faliszewski
Copyright Year
2017
Book
Applications of Evolutionary Computation

Print ISBN: 978-3-319-55848-6

Electronic ISBN: 978-3-319-55849-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-55849-3_18

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