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Erschienen in:
Enhanced Multiobjective Population-Based Incremental Learning with Applications in Risk Treaty Optimization Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Multiwinner Voting in Genetic Algorithms for Solving Ill-Posed Global Optimization Problems

verfasst von : Piotr Faliszewski, Jakub Sawicki, Robert Schaefer, Maciej Smołka

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

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Abstract

Genetic algorithms are a group of powerful tools for solving ill-posed global optimization problems in continuous domains. In case in which the insensitivity of the fitness function is the main obstacle, the most desired feature of a genetic algorithm is its ability to explore plateaus of the fitness function, surrounding its minimizers. In this paper we suggest a way of maintaining diversity of the population in the plateau regions, based on a new approach for the selection based on the theory of multiwinner elections among autonomous agents. The paper delivers a detailed description of the new selection algorithm, computational experiments that guide the choice of the proper multiwinner rule to use, and a preliminary experiment showing the proposed algorithm’s effectiveness in exploring a fitness function’s plateau.

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Vorheriges Kapitel Measuring Diversity of Socio-Cognitively Inspired ACO Search
Nächstes Kapitel A Decentralized PSO with Decoder for Scheduling Distributed Electricity Generation
Fußnoten
1
From the point of view of the elections theory, our setting is an example of two-dimensional Euclidean single-peaked preferences. Under two-dimensional Euclidean preferences, every voter and every candidate is a point in a two-dimensional Euclidean space and every voter (in our case, every individual) derives his or her preference orders by sorting the candidates (in our case, the individuals) with respect to their Euclidean distance from him or herself.
 
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Metadaten
Titel
Multiwinner Voting in Genetic Algorithms for Solving Ill-Posed Global Optimization Problems
verfasst von
Piotr Faliszewski
Jakub Sawicki
Robert Schaefer
Maciej Smołka
Copyright-Jahr
2016
Buch
Applications of Evolutionary Computation

Print ISBN: 978-3-319-31203-3

Electronic ISBN: 978-3-319-31204-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-31204-0_27

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