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Exploratory Analysis of an On-line Evolutionary Algorithm for in Simulated Robots

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Author: Haasdijk, E. · Smit, S.K. · Eiben, A.E.
Type:article
Date:2012
Source:Evolutionary Intelligence, December, 4, 5, 213-230
Identifier: 465784
doi: doi:10.1007/s12065-012-0083-6
Keywords: Evolutionary robotics · On-line evolution · Scientific testing · Parameter tuning · Bonesa · Defence Research · Defence, Safety and Security · Organisation · MSG - Modelling Simulation & Gaming · BSS - Behavioural and Societal Sciences

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 (l ? 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 (l ? 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 s.