Title :
Knowledge-based fast evaluation for evolutionary learning
Author :
Giráldez, Raúl ; Aguilar-Ruiz, Jesús S. ; Riquelme, José C.
Author_Institution :
Dept. of Comput. Sci., Univ. of Seville, Spain
fDate :
5/1/2005 12:00:00 AM
Abstract :
The increasing amount of information available is encouraging the search for efficient techniques to improve the data mining methods, especially those which consume great computational resources, such as evolutionary computation. Efficacy and efficiency are two critical aspects for knowledge-based techniques. The incorporation of knowledge into evolutionary algorithms (EAs) should provide either better solutions (efficacy) or the equivalent solutions in shorter time (efficiency), regarding the same evolutionary algorithm without incorporating such knowledge. In this paper, we categorize and summarize some of the incorporation of knowledge techniques for evolutionary algorithms and present a novel data structure, called efficient evaluation structure (EES), which helps the evolutionary algorithm to provide decision rules using less computational resources. The EES-based EA is tested and compared to another EA system and the experimental results show the quality of our approach, reducing the computational cost about 50%, maintaining the global accuracy of the final set of decision rules.
Keywords :
data mining; data structures; decision making; evolutionary computation; learning (artificial intelligence); data mining method; data structures; decision rules; efficient evaluation structure; evolutionary algorithms; evolutionary computation; knowledge incorporation; knowledge-based techniques; supervised learning; Computational efficiency; Computer science; Data mining; Data structures; Evolutionary computation; Genetic algorithms; Machine learning; Predictive models; Supervised learning; System testing; Data structures; evolutionary algorithms (EAs); knowledge incorporation; supervised learning;
Journal_Title :
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
DOI :
10.1109/TSMCC.2004.841904