Generalization capabilities of co‐evolution in learning robot behavior
Author(s) -
Berlanga A.,
Sanchis A.,
Isasi P.,
Molina J.M.
Publication year - 2002
Publication title -
journal of robotic systems
Language(s) - English
Resource type - Journals
eISSN - 1097-4563
pISSN - 0741-2223
DOI - 10.1002/rob.10054
Subject(s) - generality , generalization , process (computing) , evolutionary robotics , artificial intelligence , computer science , obstacle avoidance , robot , obstacle , artificial neural network , control engineering , engineering , mobile robot , mathematics , psychology , law , mathematical analysis , political science , psychotherapist , operating system
In this article, a co‐evolutive method is used to evolve neural controllers for generalobstacle‐avoidance of a Braitenberg vehicle. During a first evolutionary process, Evolution Strategieswere applied to generate neural controllers; the generality of the obtained behaviors was quite poor.During a second evolutionary process, a new co‐evolutive method, called Uniform Co‐evolution, isintroduced to co‐evolve both the controllers and the environment. A comparison of both methods shows thatthe co‐evolutive approach improves the generality of controllers. © 2002 Wiley Periodicals, Inc.
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