Dealing with Noisy Fitness in the Design of a RTS Game Bot

  • Antonio M. Mora
  • Antonio Fernández-Ares
  • Juan-Julián Merelo-Guervós
  • Pablo García-Sánchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)


This work describes an evolutionary algorithm (EA) for evolving the constants, weights and probabilities of a rule-based decision engine of a bot designed to play the Planet Wars game. The evaluation of the individuals is based on the result of some non-deterministic combats, whose outcome depends on random draws as well as the enemy action, and is thus noisy. This noisy fitness is addressed in the EA and then, its effects are deeply analysed in the experimental section. The conclusions shows that reducing randomness via repeated combats and re-evaluations reduces the effect of the noisy fitness, making then the EA an effective approach for solving the problem.


Genetic Algorithm Evolutionary Algorithm Aggregate Number Human Player Decision Engine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Antonio M. Mora
    • 1
  • Antonio Fernández-Ares
    • 1
  • Juan-Julián Merelo-Guervós
    • 1
  • Pablo García-Sánchez
    • 1
  1. 1.Departamento de Arquitectura y Tecnología de ComputadoresUniversidad de GranadaSpain

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