Learning what to ignore: Memetic climbing in topology and weight space

Julian Togelius, Faustino Gomez, Jürgen Schmidhuber

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

3 Scopus citations


We present the memetic climber, a simple search algorithm that learns topology and weights of neural networks on different time scales. When applied to the problem of learning control for a simulated racing task with carefully selected inputs to the neural network, the memetie climber outperforms a standard hill-climber. When inputs to the network are less carefully selected, the difference is drastic. We also present two variations of the memetie climber and discuss the generalization of the underlying principle to population-based neuroevolution algorithms. © 2008 IEEE.
Original languageEnglish (US)
Title of host publication2008 IEEE Congress on Evolutionary Computation, CEC 2008
Number of pages8
StatePublished - Nov 14 2008
Externally publishedYes

Bibliographical note

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


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