A comparative study of evolutionary optimization techniques in dynamic environments

ABSTRACT

Genetic Algorithms have widely been used for solving optimization problems in stationary environments. In recent years, there has been a growing interest for investigating and improving the performance of these algorithms in dynamic environments where the fitness landscape changes. In this study, we present an extensive comparison of several algorithms with different characteristics on a common platform by using the moving peaks benchmark and by varying problem parameters.

REFERENCES

• 1: J. Branke. Evolutionary Algorithms for dynamic optimization problems - a survey. Technical Report 387, Institute AIFB, University of Kalsruhe, February 1999.
• 2: Jurgen Branke, Evolutionary Optimization in Dynamic Environments, Kluwer Academic Publishers, Norwell, MA, 1920
• 3: Manuel Lozano , Francisco Herrera , Natalio Krasnogor , Daniel Molina, Real-coded memetic algorithms with crossover hill-climbing, Evolutionary Computation, v.12 n.3, p.273-302, September 2004 [doi>10.1162/1063656041774983]
• 4: Rasmus K. Ursem. Multi-national GAs: Multimodal optimization techniques in dynamic environments. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 19--26, Las Vegas, Nevada, USA, 10-12 2000. Morgan Kaufmann.
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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Heuristic methods

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Global optimization

General Terms:
Experimentation, Performance

Keywords:
dynamic environments, genetic algorithms, multimodal optimization, performance evaluation