Both robust computation and mutation operation in dynamic evolutionary algorithm are based on orthogonal design

ABSTRACT

A robust dynamic evolutionary algorithm (labeled RODEA), where both the robust calculation and mutation operator are based on an orthogonal design, is proposed in this paper. Previous techniques calculate the mean effective objective (for robust) by using samples without much evenly distributing over the neighborhood. The samples by using orthogonal array distribute evenly. Therefore the calculation of mean effective objective more robust. The new technique is generalized from the ODEA algorithm [1]. An orthogonal design method is employed on the niches for the mutation operator to find a potentially good solution that may become the representative in the niche. The fitness of the offspring is therefore likely to be higher than that of its parent. We propose a complex benchmark, consisting of moving function peaks, to test our new approach. Numerical experiments show that the moving solutions of the algorithm are a little worse in objective value but robust.

REFERENCES

• 1: SanYou Zeng, Hugo de GARIS, Jun He, LiShan Kang. A Novel Evolutionary Algorithm Based on an Orthogonal Design for Dynamic Optimization Problems (ODEA). In Proceedings of the 2005 Congress on Evolutionary Computatoin (CEC05). 2005, UK, Vol 2,pp1188--1195
• 2: K. Deb and H. Gupta. Searching for robust Pareto-Optimal Solutions in Multi-objective optimization. Proceedings of the 2005 Conference on Evolutionary Multi-Criterion Optimization, LNCS series, Springer-Verlag, pages 150--164, 2005.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.0 General
          Subjects: Cognitive simulation

General Terms:
Algorithms

Keywords:
dynamics, genetic algorithms, optimization methods, robust solutions