Knowledge incorporation in evolutionary computation

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This carefully edited book presents recent advances in knowledge incorporation within evolutionary computation, offering a unified framework. It provides a comprehensive, self-contained overview, including a concise introduction to evolutionary algorithms and knowledge representation methods. This resource is invaluable for researchers, students, and professionals in engineering and computer science, particularly in artificial intelligence, soft computing, natural computing, and evolutionary computation. The contents are organized into several parts: Part I introduces evolutionary computation; Part II discusses knowledge incorporation in initialization, recombination, and mutation, featuring topics like collective memory in genetic programming and cultural algorithms for job shop scheduling. Part III focuses on knowledge incorporation in selection and reproduction, including learning probabilistic models to enhance evolutionary computation and linkage learning in forest management. Part IV addresses knowledge incorporation in fitness evaluations, highlighting neural networks for fitness approximation. Part V explores lifetime learning and human-computer interactions, while Part VI examines preference incorporation in multi-objective evolutionary computation, integrating user preferences into evolutionary multi-objective optimization.