Novelty-Organizing Team of Classifiers in noisy and dynamic environments

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

In the real world, the environment is constantly changing with the input variables under the effect of noise. However, few algorithms were shown to be able to work under those circumstances. Here, Novelty-Organizing Team of Classifiers (NOTC) is applied to the continuous action mountain car as well as two variations of it: A noisy mountain car and an unstable weather mountain car. These problems take respectively noise and change of problem dynamics into account. Moreover, NOTC is compared with NeuroEvolution of Augmenting Topologies (NEAT) in these problems, revealing a trade-off between the approaches. While NOTC achieves the best performance in all of the problems, NEAT needs less trials to converge. It is demonstrated that NOTC achieves better performance because of its division of the input space (creating easier problems). Unfortunately, this division of input space also requires a bit of time to bootstrap.

Original languageEnglish
Title of host publication2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2937-2944
Number of pages8
ISBN (Electronic)9781479974924
DOIs
Publication statusPublished - Sep 10 2015
EventIEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan
Duration: May 25 2015May 28 2015

Publication series

Name2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Other

OtherIEEE Congress on Evolutionary Computation, CEC 2015
CountryJapan
CitySendai
Period5/25/155/28/15

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computational Mathematics

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