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The Importance of Scalability When Comparing Dynamic Weighted Aggregation and Pareto Front Techniques

  • Grzegorz Drzadzewski
  • Mark Wineberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3871)

Abstract

The performance of the Dynamic Weight Aggregation system as applied to a Genetic Algorithm (DWAGA) and NSGA-II are evaluated and compared against each other. The algorithms are run on 11 two-objective test functions, and 2 three-objective test functions to observe the scalability of the two systems. It is discovered that, while the NSGA-II performs better on most of the two-objective test functions, the DWAGA can outperform the NSGA-II on the three-objective problems. We hypothesize that the DWAGA’s archive helps keep the searching population size down since it does not have to both search and store the Pareto front simultaneously, thus improving both the computation time and the quality of the front.

Keywords

Pareto Front Multiobjective Optimization Objective Problem Evolutionary Multiobjective Optimization Pareto Archive Evolution Strategy 
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 2006

Authors and Affiliations

  • Grzegorz Drzadzewski
    • 1
  • Mark Wineberg
    • 1
  1. 1.Computing and Information ScienceUniversity of GuelphGuelph, OntarioCanada

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