On Using Populations of Sets in Multiobjective OptimizationReport as inadecuate




On Using Populations of Sets in Multiobjective Optimization - Download this document for free, or read online. Document in PDF available to download.

1 TIK - Computer Engineering and Networks Laboratory 2 TAO - Machine Learning and Optimisation LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623

Abstract : Most existing evolutionary approaches to multiobjective optimization aim at finding an appropriate set of compromise solutions, ideally a subset of the Pareto-optimal set. That means they are solving a set problem where the search space consists of all possible solution sets. Taking this perspective, multiobjective evolutionary algorithms can be regarded as hill-climbers on solution sets: the population is one element of the set search space and selection as well as variation implement a specific type of set mutation operator. Therefore, one may ask whether a ‘real- evolutionary algorithm on solution sets can have advantages over the classical single-population approach. This paper investigates this issue; it presents a multi-population multiobjective optimization framework and demonstrates its usefulness on several test problems and a sensor network application.





Author: Johannes Bader - Dimo Brockhoff - Samuel Welten - Eckart Zitzler -

Source: https://hal.archives-ouvertes.fr/



DOWNLOAD PDF




Related documents