DocumentCode :
2669178
Title :
Coevolutive adaptation of fitness landscape for solving the testing problem
Author :
Berlanga, A. ; Isasi, P. ; Sanchis, A. ; Molina, J.M.
Author_Institution :
Dept. de Inf., Univ. Carlos III de Madrid, Spain
Volume :
5
fYear :
2000
fDate :
2000
Firstpage :
3846
Abstract :
A general framework, called Uniform Coevolution, is introduced to overcome the testing problem in evolutionary computation methods. This framework is based on competitive evolution ideas where the solution and example sets are evolving by means of a competition to generate difficult test beds for the solutions in a gradual way. The method has been tested with two different problems: the robot navigation problem and the density parity problem in cellular automata. In both test cases using evolutive methods, the examples used in the learning process biased the solutions found. The main characteristics of the Uniform Coevolution method are that it smoothes the fitness landscape and, that it obtains “ideal learner examples”. Results using uniform coevolution show a high value of generality, compared with non co-evolutive approaches
Keywords :
cellular automata; competitive algorithms; evolutionary computation; learning by example; navigation; path planning; Uniform Coevolution; cellular automata; coevolutive adaptation; competitive evolution; density parity problem; evolutionary computation; fitness landscape; generality; ideal learner examples; learning process; robot navigation problem; testing problem; Automatic testing; Computational efficiency; Evolutionary computation; Motion planning; Neural networks; Robotics and automation; Robots; Sampling methods; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 2000 IEEE International Conference on
Conference_Location :
Nashville, TN
ISSN :
1062-922X
Print_ISBN :
0-7803-6583-6
Type :
conf
DOI :
10.1109/ICSMC.2000.886610
Filename :
886610
Link To Document :
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