Ring optimization with extinction

Extinction is a natural process that drives biological evolution. In this study, the impact of four different extinction operators on the evolution of side-effect machines with a ring optimizer was investigated. Side-effect machines are an emerging technology used to generate features for DNA classification. Ring optimization is a type of evolutionary algorithm inspired by the biological concept of ring species. Previous work showed that ring optimization was an efficient technique for locating good side effect machines with substantial robustness against parameter choice for the optimizer. This study extends that research by incorporating extinction, which has been shown to substantially improve the performance of the ring optimizer on discrete and numerical test problems. Two of the four extinction operators improved the quality of the best outcome, while all four were able to reset the ring optimizer into a more exploratory state.

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Hybridization and ring optimization for larger sets of embeddable biomarkers

Ashlock

Houghten

2017

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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Ring optimization with extinction

Cited by 4 publications

References 16 publications

The impact of elite fraction and population size on evolved iterated prisoner's dilemma agents

The impact of elite fraction and population size on evolved iterated prisoner's dilemma agents

Evolving DNA classifiers with extinction based ring optimization

Hybridization and ring optimization for larger sets of embeddable biomarkers

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