Fitness functions in evolutionary robotics: A survey and analysis

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@Article{Nelson2009345,
  author =       "Andrew L. Nelson and Gregory J. Barlow and 
                 Lefteris Doitsidis",
  title =        "Fitness functions in evolutionary robotics: A survey
                 and analysis",
  journal =      "Robotics and Autonomous Systems",
  volume =       "57",
  number =       "4",
  pages =        "345--370",
  year =         "2009",
  ISSN =         "0921-8890",
  DOI =          "doi:10.1016/j.robot.2008.09.009",
  URL =          "http://www.sciencedirect.com/science/article/B6V16-4TTMJV3-1/2/2549524d8e0f3982730659e49ad3fa75",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 robotics, Fitness functions, Autonomous learning
                 robots, Artificial life",
  abstract =     "This paper surveys fitness functions used in the field
                 of evolutionary robotics (ER). Evolutionary robotics is
                 a field of research that applies artificial evolution
                 to generate control systems for autonomous robots.
                 During evolution, robots attempt to perform a given
                 task in a given environment. The controllers in the
                 better performing robots are selected, altered and
                 propagated to perform the task again in an iterative
                 process that mimics some aspects of natural evolution.
                 A key component of this process-one might argue, the
                 key component-is the measurement of fitness in the
                 evolving controllers. ER is one of a host of machine
                 learning methods that rely on interaction with, and
                 feedback from, a complex dynamic environment to drive
                 synthesis of controllers for autonomous agents. These
                 methods have the potential to lead to the development
                 of robots that can adapt to characterised environments
                 and which may be able to perform tasks that human
                 designers do not completely understand. In order to
                 achieve this, issues regarding fitness evaluation must
                 be addressed. In this paper we survey current ER
                 research and focus on work that involved real robots.
                 The surveyed research is organised according to the
                 degree of a priori knowledge used to formulate the
                 various fitness functions employed during evolution.
                 The underlying motivation for this is to identify
                 methods that allow the development of the greatest
                 degree of novel control, while requiring the minimum
                 amount of a priori task knowledge from the designer.",
}

Genetic Programming entries for Andrew L Nelson Gregory J Barlow Lefteris Doitsidis

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