Fitness functions in evolutionary robotics: A survey and analysis
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- @Article{Nelson2009345,
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author = "Andrew L. Nelson and Gregory J. Barlow and
Lefteris Doitsidis",
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title = "Fitness functions in evolutionary robotics: A survey
and analysis",
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journal = "Robotics and Autonomous Systems",
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volume = "57",
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number = "4",
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pages = "345--370",
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year = "2009",
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ISSN = "0921-8890",
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DOI = "doi:10.1016/j.robot.2008.09.009",
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URL = "http://www.sciencedirect.com/science/article/B6V16-4TTMJV3-1/2/2549524d8e0f3982730659e49ad3fa75",
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keywords = "genetic algorithms, genetic programming, Evolutionary
robotics, Fitness functions, Autonomous learning
robots, Artificial life",
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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
Citations