DECA: The doping-driven evolutionary control algorithm

Pieter Spronck*, Ida Sprinkhuizen-Kuyper, Eric Postma

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    1 Citation (Web of Science)


    In task control., evolutionary optimization tends to favor controllers that solve the easier task instances but that fail to solve the harder ones. We call this the problem of hard instances. The doping-driven evolutionary control algorithm (DECA) is introduced to deal with the problem. The effectiveness of DECA is assessed on two task-control problems: a box-pushing task and a food-gathering task. The experimental results show DECA to generate controllers that can solve both the easy and hard instances of both task-control problems. We discuss the results by offering a qualitative explanation for DECA's success and comparing it to related techniques. We conclude that the problem of hard instances is alleviated by the application of DECA.

    Original languageEnglish
    Pages (from-to)169-197
    Number of pages29
    JournalApplied Artificial Intelligence
    Issue number3
    Publication statusPublished - Mar 2008

    Cite this