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Publication Details for Inproceedings "Maximizing Population Diversity in Single-Objective Optimization"

 

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Authors: Tamara Ulrich, Lothar Thiele
Group: Computer Engineering
Type: Inproceedings
Title: Maximizing Population Diversity in Single-Objective Optimization
Year: 2011
Month: July
Pub-Key: ut2011a.pdf
Book Titel: Genetic and Evolutionary Computation Conference (GECCO)
Pages: 641-648
Keywords: Diversity in Decision Space, Evolutionary Algorithms
Publisher: ACM
Abstract: Typically, optimization attempts to find a solution which minimizes the given objective function. But often, it might also be useful to obtain a set of structurally very diverse solu- tions which all have acceptable objective values. With such a set, a decision maker would be given a choice of solutions to select from. In addition, he can learn about the optimization problem at hand by inspecting the diverse close-to-optimal solutions. This paper proposes NOAH, an evolutionary algorithm which solves a mixed multi-objective problem: Determine a maximally diverse set of solutions whose objective values are below a provided objective barrier. It does so by iter- atively switching between objective value and set-diversity optimization while automatically adapting a constraint on the objective value until it reaches the barrier. Tests on an nk-Landscapes problem and a 3-Sat problem as well as on a more realistic bridge construction problem show that the algorithm is able to produce high quality solutions with a significantly higher structural diversity than standard evo- lutionary algorithms.
Location: Dublin, Ireland
Resources: [BibTeX] [Paper as PDF]

 

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