Title :
A preliminary study of learnable evolution methodology implemented with C4.5
Author_Institution :
Dept. of Comput. Sci., George Mason Univ., Fairfax, VA, USA
Abstract :
The learnable evolution model (LEM) introduces a machine learning-based birth operator into an evolutionary computing algorithm. New individuals are generated from hypotheses learned by the operator from the most-fit and least-fit parent sub-populations. The LEM allows for arbitrary machine learning mechanisms, though, so far, only an AQ (Algorithm Quasi-optimal) based machine learner has been used in LEM implementations. This paper describes preliminary results using a different machine learner in a LEM implementation - C4.5
Keywords :
evolutionary computation; learning (artificial intelligence); mathematical operators; AQ-based learner; C4.5 machine learner; arbitrary machine learning mechanisms; birth operator; evolutionary computing algorithm; learnable evolution model; learned hypotheses; least-fit parent sub-population; most-fit parent sub-population; new individual generation; Computer science; Cultural differences; Evolutionary computation; Laboratories; Learning systems; Machine learning; Machine learning algorithms; Optimization methods; Runtime; Switches;
Conference_Titel :
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7282-4
DOI :
10.1109/CEC.2002.1006992
