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Modeling Regularity to Improve Scalability of Model-Based Multiobjective Optimization Algorithms.

Authors :
Rozenberg, G.
Bäck, Th.
Eiben, A.E.
Kok, J.N.
Spaink, H.P.
Amari, S.
Brassard, G.
Jong, K.A. De
Gielen, C.C.A.M.
Head, T.
Kari, L.
Landweber, L.
Martinetz, T.
Martinetz, Z.
Mozer, M.C.
Oja, E.
Păun, G.
Reif, J.
Rubin, H.
Salomaa, A.
Source :
Multiobjective Problem Solving from Nature; 2008, p331-355, 25p
Publication Year :
2008

Abstract

Model-based multiobjective optimization is one class of metaheuristics for solving multiobjective optimization problems, where a probabilistic model is built from the current distribution of the solutions and new candidate solutions are generated from the model. One main difficulty in model-based optimization is constructing a probabilistic model that is able to effectively capture the structure of the problems to enable efficient search. This chapter advocates a new type of probabilistic model that takes the regularity in the distribution of Pareto-optimal solutions into account. We compare our model to two other model-based multiobjective algorithms on a number of test problems to demonstrate that it is scalable to high-dimensional optimization problems with or without linkage linkage among the design variables. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540729631
Database :
Supplemental Index
Journal :
Multiobjective Problem Solving from Nature
Publication Type :
Book
Accession number :
33678446
Full Text :
https://doi.org/10.1007/978-3-540-72964-8_16