Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
Analysis of noisy time-series signals with GA involving viral infection with tropism
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Improving small population performance under noise with viral infection + tropism
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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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.