Hierarchical Knowledge in Self-Improving Grammar-Based Genetic Programming
Refereed conference paper presented and published in conference proceedings


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其它資訊
摘要Structure of a grammar can influence how well a Grammar-Based Genetic Programming system solves a given problem but it is not obvious to design the structure of a grammar, especially when the problem is large. In this paper, our proposed Bayesian Grammar-Based Genetic Programming with Hierarchical Learning (BGBGP-HL) examines the grammar and builds new rules on the existing grammar structure during evolution. Once our system successfully finds the good solution(s), the adapted grammar will provide a grammar-based probabilistic model to the generation process of optimal solution(s). Moreover, our system can automatically discover new hierarchical knowledge (i.e. how the rules are structurally combined) which composes of multiple production rules in the original grammar. In the case study using deceptive royal tree problem, our evaluation shows that BGBGP-HL achieves the best performance among the competitors while it is capable of composing hierarchical knowledge. Compared to other algorithms, search performance of BGBGP-HL is shown to be more robust against deceptiveness and complexity of the problem.
著者Wong PK, Wong ML, Leung KS
會議名稱14th International Conference on Parallel Problem Solving from Nature (PPSN)
會議開始日17.09.2016
會議完結日21.09.2016
會議地點Edinburgh
會議國家/地區英國
詳細描述organized by the School of Computing, Edinburgh Napier University, Scotland, UK,
出版年份2016
卷號9921
出版社SPRINGER INT PUBLISHING AG
頁次270 - 280
國際標準書號978-3-319-45822-9
電子國際標準書號978-3-319-45823-6
國際標準期刊號0302-9743
語言英式英語
關鍵詞Adaptive grammar; Bayesian network; Estimation of distribution programming; Genetic Programming; Hierarchical knowledge learning
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods

上次更新時間 2020-29-10 於 01:33