Dominance learning in diploid genetic algorithms for dynamic optimization problems

ABSTRACT

This paper proposes an adaptive dominance mechanism for diploidy genetic algorithms in dynamic environments. In this scheme, the genotype to phenotype mapping in each gene locus is controlled by a dominance probability, which is learned adaptively during the searching progress and hence is adapted to the dynamic environment. Using a series of dynamic test problems, the proposed dominance scheme is compared to two other dominance schemes for diploidy genetic algorithms. The experimental results validate the efficiency of the proposed dominance learning scheme.

REFERENCES

• 1: Jonathan Lewis , Emma Hart , Graeme Ritchie, A Comparison of Dominance Mechanisms and Simple Mutation on Non-stationary Problems, Proceedings of the 5th International Conference on Parallel Problem Solving from Nature, p.139-148, September 27-30, 1998
• 2: Khim Peow Ng , Kok Cheong Wong, A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization, Proceedings of the 6th International Conference on Genetic Algorithms, p.159-166, July 15-19, 1995
• 3: C. Ryan. The degree of oneness. Proc. of the 1994 ECAI Workshop on Genetic Algorithms, 1994.
• 4: Shengxiang Yang , Xin Yao, Experimental study on population-based incremental learning algorithms for dynamic optimization problems, Soft Computing - A Fusion of Foundations, Methodologies and Applications, v.9 n.11, p.815-834, November 2005 [doi>10.1007/s00500-004-0422-3]

INDEX TERMS

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

General Terms:
Algorithms

Keywords:
diploid genetic algorithms, dominance change scheme, dominance learning, dynamic optimization problems