A Function-Based Classifier Learning Scheme Using Genetic Programming
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Adversarial Search by Evolutionary Computation
Evolutionary Computation
A Dual-Objective Evolutionary Algorithm for Rules Extraction in Data Mining
Computational Optimization and Applications
AN IMPROVED KNOWLEDGE-ACQUISITION STRATEGY BASED ON GENETIC PROGRAMMING
Cybernetics and Systems
Real-coded genetic algorithm for parametric modelling of a TRMS
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary computing for knowledge discovery in medical diagnosis
Artificial Intelligence in Medicine
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An actual knowledge application is made by means of evolution paradigms in terms of knowledge acquisition. An automatic knowledge integration approach in a distributed-knowledge environment is thus proposed to integrate multiple rule sets into a single effective rule set. The proposed approach consists of two phases: knowledge encoding and knowledge integration. In the encoding phase, each knowledge input is translated and expressed as a rule set, then encoded as a bit string. The combined bit strings from multiple knowledge inputs form an initial knowledge population, which is then ready for integration. In the knowledge integration phase, a genetic search technique generates an optimal or nearly optimal rule set from these initial knowledge-input strings. Finally, experimental results from diagnosis of brain tumors show that the rule set derived by the proposed approach is much more accurate than each initial rule set