Evolutionary Procedural 2D Map Generation using Novelty Search

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Abstract

This paper presents an evolutionary approach to procedural content generation of 2D maps for computer games. To provide better adaptability to the map designer’s vision, user preference is incorporated to guide the algorithm. A cooperative method utilizes novelty search as a source of diverse solutions, which are then further optimized by multiple, subsequent genetic algorithms. We compare the results to a second approach based on multi-objective optimization, which takes the two conflicting goals of optimizing towards user preference and finding novel solutions as objective functions to build a Pareto front of maps.
Original languageEnglish
Title of host publicationGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
EditorsTobias Friedrich
PublisherACM Sigevo
Pages39-40
Number of pages2
ISBN (Electronic)9781450343237
ISBN (Print)978-1-4503-4323-7
DOIs
Publication statusPublished - 20 Jul 2016
EventGenetic and Evolutionary Computation Conference (GECCO 2016) - Denver, Colorado, United States
Duration: 20 Jul 201624 Jul 2016
http://gecco-2016.sigevo.org/

Publication series

NameGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference

Conference

ConferenceGenetic and Evolutionary Computation Conference (GECCO 2016)
Country/TerritoryUnited States
CityDenver, Colorado
Period20.07.201624.07.2016
Internet address

Keywords

  • Procedural Content Generation
  • Search-based Procedural Content Generation
  • Novelty Search
  • Genetic Algorithm
  • HeuristicLab
  • Genetic algorithm
  • Heuristiclab
  • Novelty search
  • Search-based procedural content generation
  • Procedural content generation

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