Evolutionary concept learning in first order logic: an overview
AI Communications
Fast rule matching for learning classifier systems via vector instructions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Intelligent technologies for investing: a review of engineering literature
Intelligent Decision Technologies
Large scale data mining using genetics-based machine learning
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Large scale data mining using genetics-based machine learning
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Knowledge-Inducing interactive genetic algorithms based on multi-agent
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Interactive genetic algorithms based on implicit knowledge model
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
An approach to reduce the cost of evaluation in evolutionary learning
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Large scale data mining using genetics-based machine learning
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Large scale data mining using genetics-based machine learning
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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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.