Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Expert Systems: Principles and Programming
Expert Systems: Principles and Programming
Evolutionary Computation in Economics and Finance
Evolutionary Computation in Economics and Finance
KnowledgeScope: managing knowledge in context
Decision Support Systems
Genetic programming techniques for hand written digit recognition
Signal Processing
Evolving rule-based systems in two medical domains using genetic programming
Artificial Intelligence in Medicine
Applying genetic programming technique in classification trees
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on intelligent systems for financial engineering and computational finance
Building credit scoring models using genetic programming
Expert Systems with Applications: An International Journal
Automatically integrating multiple rule sets in adistributed-knowledge environment
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Knowledge acquisition can deal with the task of extracting desirable or useful knowledge from data sets for a practical application. In this paper, we have modified our previous gp-based learning strategy to search for an appropriate classification tree. The proposed approach consists of three phases: knowledge creation, knowledge evolution, and knowledge output. In the creation phase, a set of classification trees are randomly generated to form an initial knowledge population. In the evolution phase, the genetic programming technique is used to generate a good classification tree. In the output phase, the final derived classification tree is transferred as a rule set, then outputted to the knowledge base to facilitate the inference of new data. One new genetic operator, separation, is designed in this proposed approach to remove contradiction, thus producing more accurate classification rules. Experimental results from the diagnosis of breast cancers also show the feasibility of the proposed algorithm.