Continual Online Evolutionary Planning for In-Game Build Order Adaptation in StarCraft

Niels Orsleff Justesen, Sebastian Risi

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


The real-time strategy game StarCraft has become an important benchmark for AI research as it poses a complex environment with numerous challenges. An important strategic aspect in this game is to decide what buildings and units to produce. StarCraft bots playing in AI competitions today are only able to switch between predefined strategies, which makes it hard to adapt to new situations. This paper introduces an evolutionary-based method to overcome this challenge, called Continual Online Evolutionary Planning (COEP), which is able to perform in-game adaptive build-order planning. COEP was added to an open source StarCraft bot called UAlbertaBot and is able to outperform the built-in bots in the game as well as being competitive against a number of scripted opening strategies. The COEP augmented bot can change its build order dynamically and quickly adapt to the opponent’s strategy.
Original languageEnglish
Title of host publicationGECCO ’17 Proceedings of the Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Publication date16 Jul 2017
ISBN (Print)978-1-4503-4920-8
Publication statusPublished - 16 Jul 2017
EventThe Genetic and Evolutionary Computation Conference - Berlin, Germany, Germany
Duration: 15 Jul 201719 Jul 2017


ConferenceThe Genetic and Evolutionary Computation Conference
Internet address


  • StarCraft
  • real-time strategy
  • adaptive AI
  • evolutionary planning
  • build-order optimization


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