Research on stronger convergence in probability of immune genetic algorithm

ABSTRACT

Immune Genetic Algorithm (IGA) is a new optimization strategy by simulating the behavior of biological immune system. Aiming at the relatively scarce work on the discussion of convergence on IGA, strong convergence in probability of IGA was proved on the condition that the time tended to infinity comparing to the previous conclusion that IGA was weak convergence in probability by (1)modeling the immune operators and optimization process and (2)introducing a lemma with 2 immune parameters to analyze some characteristics of the complement set of global optima set. This conclusion will be helpful to understand the performance of IGA and set better immune parameters.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Deduction (e.g., natural, rule-based)

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Uncertainty, "fuzzy," and probabilistic reasoning

General Terms:
Theory, Verification

Keywords:
convergence, immune genetic algorithm, model, parameter analysis, strong convergence in probability