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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