Evolutionary Intelligence

, Volume 5, Issue 4, pp 213–230 | Cite as

Exploratory analysis of an on-line evolutionary algorithm in simulated robots

Research Paper

Abstract

In traditional evolutionary robotics, robot controllers are evolved in a separate design phase preceding actual deployment; we call this off-line evolution. Alternatively, robot controllers can evolve while the robots perform their proper tasks, during the actual operational phase; we call this on-line evolution. In this paper we describe three principal categories of on-line evolution for developing robot controllers (encapsulated, distributed, and hybrid), present an evolutionary algorithm belonging to the first category (the (μ + 1) on-line algorithm), and perform an extensive study of its behaviour. In particular, we use the Bonesa parameter tuning method to explore its parameter space. This delivers near-optimal settings for our algorithm in a number of tasks and, even more importantly, it offers profound insights into the impact of our algorithm’s parameters and features. Our experimental analysis of (μ + 1) on-line shows that it seems preferable to try many alternative solutions and spend little effort on refining possibly faulty assessments; that there is no single combination of parameters that performs well on all problem instances and that the most influential parameter of this algorithm—and therefore the prime candidate for a control scheme—is the evaluation length τ.

Keywords

Evolutionary robotics On-line evolution Scientific testing Parameter tuning Bonesa 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Vrije UniversiteitAmsterdamThe Netherlands
  2. 2.TNOThe HagueThe Netherlands

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