Improving small population performance under noise with viral infection + tropism

ABSTRACT

In this paper we report on the effect of viral infection with tropism on the formation of building blocks in genetic operations. In previous research, we applied genetic algorithms to the analysis of time-series signals with noise. We demonstrated the possibility of reducing the number of required entities and improving the rate of convergence when searching for a solution by having some of the host chromosomes harbor viruses with a tropism function. Here, we simulate problems having both multimodality and deceptiveness features and problems that include noise as test functions, and show that viral infection with tropism can increase the proportion of building blocks in the population when it cannot be assumed that a necessary and sufficient number of entities are available to find a solution. We show that this capability is especially noticeable in problems that include noise.

REFERENCES

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INDEX TERMS

Primary Classification:
  A. General Literature
  A.0 GENERAL
      Subjects: Conference proceedings

Additional Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.3 Language Constructs and Features

General Terms:
Algorithms, Design, Experimentation, Verification

Keywords:
building block, deceptive problem, genetic algorithms, multimodality, noisy optimization, tropism, virus infection