Memetic Self-Configuring Genetic Programming for Fuzzy Classifier Ensemble Design

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• 关键词：Self ; configuring evolutionary algorithms ; Local search on discrete structures ; Fuzzy classifier ; Ensembles ; Automated design ; Performance estimation
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9140
• 期：1
• 页码：285-293
• 全文大小：186 KB
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• 作者单位：Maria Semenkina (19)
  Eugene Semenkin (19)
  
  19. Siberian State Aerospace University, Krasnoyarsk, Russia
  
• 丛书名：Advances in Swarm and Computational Intelligence
• ISBN：978-3-319-20466-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

For a fuzzy classifier automated design a hybrid self-configuring evolutionary algorithm is implemented. For the tuning of linguistic variables a self-configuring genetic algorithm is used. Ensemble members and the ensembling method are generated automatically with the self-configuring genetic programming algorithm that does not need preliminary adjusting. A hybridization of self-configuring genetic programming algorithms with a local search in the space of trees is fulfilled to improve their performance for fuzzy rule bases and ensembles automated design. The local search is implemented with two neighbourhood systems, three strategies of tree scanning (“full- “incomplete-and “truncated- and two ways of movement between adjacent trees (transition by the first improvement and the steepest descent). The performance of all developed memetic algorithms is estimated on a representative set of test problems of the function approximation as well as on real-world classification problems. The numerical experiment results show the competitiveness of the approach proposed.