Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/54871
Title: Knowledge incorporation through lifetime learning
Authors: Ku, KKW
Mak, MW 
Issue Date: 2005
Publisher: Springer
Source: In Y Jin (Ed.), Knowledge incorporation in evolutionary computation, p. 359-384. Berlin: Springer, 2005 How to cite?
Series/Report no.: Studies in fuzziness and soft computing ; v. 167
Abstract: Evolutionary computation is known to require long computation time for large problems. This chapter examines the possibility of improving the evolution process by incorporating domain-specific knowledge into evolutionary computation through lifetime learning. Different approaches to combining lifetime learning and evolution are compared. While the Lamarckian approach is able to speed up the evolution process and improve the solution quality, the Baldwinian approach is found to be inefficient. Through empirical analysis, it is conjectured that the inefficiency of the Baldwinian approach is due to the difficulties for genetic operations to produce the genotypic changes that match the phenotypic changes obtained by learning. This suggests that combining evolutionary computation inattentively with any learning method available is not a proper way to construct hybrid algorithms; rather, the correlation between the genetic operations and learning should be carefully considered.
URI: http://hdl.handle.net/10397/54871
ISBN: 3540445110 (electronic bk.)
9783540445111 (electronic bk.)
9783642061745 (print)
3540229027 (print : alk. paper)
9783540229025 (print : alk. paper)
DOI: 10.1007/978-3-540-44511-1_17
Appears in Collections:Book Chapter

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