Collective neuro-evolution for evolving specialized sensor resolutions in a multi-rover task

G.S. Nitschke, M.C. Schut, A.E. Eiben

Research output: Contribution to JournalArticleAcademicpeer-review


This article presents results from an evaluation of the collective neuro-evolution (CONE) controller design method. CONE solves collective behavior tasks, and increases task performance via facilitating emergent behavioral specialization. Emergent specialization is guided by genotype and behavioral specialization difference metrics that regulate genotype recombination. CONE is comparatively tested and evaluated with similar neuro-evolution methods in an extension of the multi-rover task, where behavioral specialization is known to benefit task performance. The task is for multiple simulated autonomous vehicles (rovers) to maximize the detection of points of interest (red rocks) in a virtual environment. Results indicate that CONE is appropriate for deriving sets of specialized rover behaviors that complement each other such that a higher task performance, comparative to related controller design methods, is attained in the multi-rover task. © Springer-Verlag 2009.
Original languageEnglish
Pages (from-to)13-29
JournalEvolutionary Intelligence
Publication statusPublished - 2010


Dive into the research topics of 'Collective neuro-evolution for evolving specialized sensor resolutions in a multi-rover task'. Together they form a unique fingerprint.

Cite this