Improving small population performance under noise with viral infection + tropism

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.

[1]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[2]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[5]  David E. Goldberg,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1999, Evolutionary Computation.

[6]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[7]  David E. Goldberg,et al.  The Design of Innovation , 2002, Genetic Algorithms and Evolutionary Computation.

[8]  Thomas D. LaToza,et al.  On the supply of building blocks , 2001 .

[9]  Toshio Fukuda,et al.  Trajectory planning of cellular manipulator system using virus-evolutionary genetic algorithm , 1996, Robotics Auton. Syst..

[10]  D. Goldberg,et al.  A practical schema theorem for genetic algorithm design and tuning , 2001 .

[11]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[12]  D. E. Goldberg,et al.  Simple Genetic Algorithms and the Minimal, Deceptive Problem , 1987 .

[13]  D. E. Goldberg,et al.  An analysis of a reordering operator on a GA-hard problem , 1990, Biological Cybernetics.

[14]  Yukinobu Hoshino,et al.  A Proposal of GA Using Symbiotic Evolutionary Viruses and its Virus Evaluation Techniques , 2004, J. Adv. Comput. Intell. Intell. Informatics.

[15]  D. Goldberg,et al.  Escaping hierarchical traps with competent genetic algorithms , 2001 .

[16]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[17]  Jeffrey Horn,et al.  The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations , 1997 .

[18]  Yuji Sato,et al.  Analysis of noisy time-series signals with GA involving viral infection with tropism , 2007, GECCO '07.

[19]  Yuji Sato,et al.  Reward allotment in an event-driven hybrid learning classifier system for online soccer games , 2006, GECCO.

[20]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[21]  David E. Goldberg,et al.  Construction of high-order deceptive functions using low-order Walsh coefficients , 1992, Annals of Mathematics and Artificial Intelligence.