A Hierarchical Probabilistic Divergent Search Applied to a Binary Classification

Senthil Murugan, Enrique Naredo, Douglas Mota Dias, Conor Ryan, Flaviano Godinez, James Vincent Patten

Research output: Contribution to journalConference articlepeer-review

Abstract

The trend in recent years of the scientific community on solving a wide range of problems through Artificial Intelligence has highlighted the benefits of open-ended search algorithms. In this paper we apply a probabilistic version for a divergent search algorithm in combination of a strategy to reduce the number of evaluations and computational effort by gathering the population from a Genetic Programming algorithm into groups and pruning the worst groups each certain number of generations. The combination proposed has shown encouraging results against a standard GP implementation on three binary classification problems, where the time taken to run an experiment is significantly reduced to only 5% of the total time from the standard approach while still maintaining, and indeed exceeding in the experimental results.

Original languageEnglish
Pages (from-to)345-353
Number of pages9
JournalInternational Conference on Agents and Artificial Intelligence
Volume2
DOIs
Publication statusPublished - 2022
Event14th International Conference on Agents and Artificial Intelligence , ICAART 2022 - Virtual, Online
Duration: 3 Feb 20225 Feb 2022

Keywords

  • Classification
  • Genetic Programming
  • Novelty Search

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