Analysis of noisy time-series signals with GA involving viral infection with tropism

ABSTRACT

In this paper we report on a study in which genetic algorithms are applied to the analysis of noisy time-series signals, which is related to the problem of analyzing the motion characteristics of moving bodies (distance, bearing, course, velocity, etc.) by covertly sampling the sound of moving objects with submarine monitoring systems that track moving objects travelling on or through the water. In particular, we propose improving the system's ability to search through noisy data by grafting viruses onto the chromosomes used in genetic algorithms. Specifically, we propose a search method that can cope robustly with noise through the cooperative action of a wide-area search implemented by host chromosomes and a local search implemented by viruses grafted onto these chromosomes. To improve the infection rate, we also impose limits on the types of host entity that can be infected by viruses. By conducting evaluation tests in computer simulations, we show that the proposed technique can achieve a better rate of convergence and is capable of searching for a solution with fewer entities.

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INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.0 GENERAL

General Terms:
Design, Experimentation, Performance, Verification

Keywords:
genetic algorithms, inverse problem, noisy optimization, time-series problem, tropism, virus evolution, virus infection