A Hyperheuristic Methodology to Generate Adaptive Strategies for Games

Jiawei Li, Graham Kendall

Research output: Journal PublicationArticlepeer-review

14 Citations (Scopus)


Hyperheuristics have been successfully applied in solving a variety of computational search problems. In this paper, we investigate a hyperheuristic methodology to generate adaptive strategies for games. Based on a set of low-level heuristics (or strategies), a hyperheuristic game player can generate strategies which adapt to both the behavior of the co-players and the game dynamics. By using a simple heuristic selection mechanism, a number of existing heuristics for specialized games can be integrated into an automated game player. As examples, we develop hyperheuristic game players for three games: iterated prisoner's dilemma, repeated Goofspiel and the competitive traveling salesmen problem. The results demonstrate that a hyperheuristic game player outperforms the low-level heuristics, when used individually in game playing and it can generate adaptive strategies even if the low-level heuristics are deterministic. This methodology provides an efficient way to develop new strategies for games based on existing strategies.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalIEEE Transactions on Computational Intelligence and AI in Games
Issue number1
Publication statusPublished - Mar 2017


  • Competitive traveling salesmen problem
  • Goofspiel
  • game
  • hyperheuristic
  • iterated prisoner's dilemma (IPD)

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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