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Journal Articles Evolutionary Intelligence Year : 2014

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Abstract

Evolutionary robotics is often viewed as the application of a family of black-box optimization algorithms -- evolutionary algorithms - - to the design of robots, or parts of robots. When considering evolutionary robotics as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most evolutionary robotics experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because evolutionary robotics experiments share common features, selective pressures for evolutionary robotics are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two sub-categories are further considered: task-specific approaches, which require knowledge on how to solve the task and task-agnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process.
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Dates and versions

hal-01150254 , version 1 (09-05-2015)

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Stephane Doncieux, Jean-Baptiste Mouret. Beyond black-box optimization: a review of selective pressures for evolutionary robotics. Evolutionary Intelligence, 2014, 7 (2), pp.71-93. ⟨10.1007/s12065-014-0110-x⟩. ⟨hal-01150254⟩
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