Evolving EFSMs solving a path-planning problem by genetic programming

Maxim Buzdalov, Andriy Sokolov

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

In this paper, we present an approach to evolving of an algorithm encoded as an extended finite-state machine that solves a simple path-planning problem - finding a path in an unknown area filled with obstacles using a constant amount of memory - by means of genetic programming. Experiments show that in 100% of cases a reasonably correct EFSM with behavior similar to one of the BUG algorithms is evolved.
Original languageEnglish
Title of host publicationGECCO '12
Subtitle of host publicationProceedings of the 14th annual conference companion on Genetic and evolutionary computation
EditorsTerence Soule
PublisherAssociation for Computing Machinery
Pages591-594
Number of pages4
ISBN (Electronic)978-1-4503-1178-6
DOIs
Publication statusPublished - 07 Jul 2012
Externally publishedYes
EventGECCO 2012 - Genetic and Evolutionary Computation Conference - Philadelphia, United States of America
Duration: 07 Jul 201211 Jul 2012

Conference

ConferenceGECCO 2012 - Genetic and Evolutionary Computation Conference
Country/TerritoryUnited States of America
CityPhiladelphia
Period07 Jul 201211 Jul 2012

Keywords

  • bug algoritms
  • finite-state machine
  • genetic programming
  • path-planning problem

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