Countering poisonous inputs with memetic neuroevolution

Julian Togelius, Tom Schaul, Jürgen Schmidhuber, Faustino Gomez

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

5 Scopus citations


Applied to certain problems, neuroevolution frequently gets stuck in local optima with very low fitness; in particular, this is true for some reinforcement learning problems where the input to the controller is a high-dimensional and/or ill-chosen state description. Evidently, some controller inputs are "poisonous", and their inclusion induce such local optima. Previously, we proposed the memetic climber, which evolves neural network topology and weights at different timescales, as a solution to this problem. In this paper, we further explore the memetic climber, and introduce its population-based counterpart: the memetic ES. We also explore which types of inputs are poisonous for two different reinforcement learning problems. © 2008 Springer-Verlag Berlin Heidelberg.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number of pages10
StatePublished - Nov 26 2008
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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