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A Statistical Comparison of Multiobjective Evolutionary Algorithms Including the MOMGA-II

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1993)

Abstract

Many real-world scientific and engineering applications involve finding innovative solutions to “hard” Multiobjective Optimization Problems (MOP). Various Multiobjective Evolutionary Algorithms (MOEA) have been developed to obtain MOP Pareto solutions. A particular exciting MOEA is the MOMGA which is an extension of the single-objective building block (BB) based messy Genetic Algorithm. The intent of this discussion is to illustrate that modifications made to the Multi-Objective messy GA (MOMGA) have further improved its efficiency resulting in the MOMGA-II. The MOMGA-II uses a probabilistic BB approach to initializing the population referred to as Probabilistically Complete Initialization. This has the effect of improving the efficiency of the MOMGA through the reduction of computational bottle-necks. Similar statistical results have been obtained using the MOMGA-II as compared to the results of the original MOMGA as well as those obtained by other MOEAs as tested with standard generic MOP test suites.

Keywords

Pareto Front Test Suite Generational Distance Pareto Optimal Solution Multiobjective Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  1. 1.Dept of Electrical and Computer EngineeringAir Force Institute of TechnologyUSA
  2. 2.Optical Radiation BranchAir Force Research LaboratoryTXUSA

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