Evolving counter-propagation neuro-controllers for multi-objective robot navigation

Amiram Moshaiov, Michael Zadok

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This study follows a recent investigation on evolutionary training of counter-propagation neural-networks for multi-objective robot navigation in various environments. Here, in contrast to the original study, the training of the counter-propagation networks is done using an improved two-phase algorithm to achieve tuned weights for both classification of inputs and the control function. The proposed improvement concerns the crossover operation among the networks, which requires special attention due to the classification layer. The numerical simulations, which are reported here, suggest that both the current and original algorithms are superior to the classical approach of using a feed-forward network. It is also observed that the current version has better convergence properties as compared with the original one.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation - 16th European Conference, EvoApplications 2013, Proceedings
PublisherSpringer Verlag
Pages589-598
Number of pages10
ISBN (Print)9783642371912
DOIs
StatePublished - 2013
Event16th European Conference on Applications of Evolutionary Computation, EvoApplications 2013 - Vienna, Austria
Duration: 3 Apr 20135 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7835 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th European Conference on Applications of Evolutionary Computation, EvoApplications 2013
Country/TerritoryAustria
CityVienna
Period3/04/135/04/13

Fingerprint

Dive into the research topics of 'Evolving counter-propagation neuro-controllers for multi-objective robot navigation'. Together they form a unique fingerprint.

Cite this