Overview

This carefully edited book puts together the state of the art and recent advances in knowledge incorporation in evolutionary computation within a unified framework. The book provides a comprehensive self-contained view of knowledge incorporation in evolutionary computation including a concise introduction to evolutionary algorithms as well as knowledge representation methods. Knowledge Incorporation in Evolutionary Computation is a valuable reference for researchers, students and professionals from engineering and computer science, in particular in the areas of artificial intelligence, soft computing, natural computing, and evolutionary computation.

Full Product Details

Author:   Yaochu Jin
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   Softcover reprint of hardcover 1st ed. 2004
Volume:   167
Dimensions:   Width: 15.50cm , Height: 2.90cm , Length: 23.50cm
Weight:   0.854kg
ISBN:  

9783642061745

ISBN 10:   3642061745
Pages:   548
Publication Date:   18 December 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

I Introduction.- A Selected Introduction to Evolutionary Computation.- II Knowledge Incorporation in Initialization, Recombination and Mutation.- The Use of Collective Memory in Genetic Programming.- A Cultural Algorithm for Solving the Job Shop Scheduling Problem.- Case-Initialized Genetic Algorithms for Knowledge Extraction and Incorporation.- Using Cultural Algorithms to Evolve Strategies in A Complex Agent-based System.- Methods for Using Surrogate Models to Speed Up Genetic Algorithm Optimization: Informed Operators and Genetic Engineering.- Fuzzy Knowledge Incorporation in Crossover and Mutation.- III Knowledge Incorporation in Selection and Reproduction.- Learning Probabilistic Models for Enhanced Evolutionary Computation.- Probabilistic Models for Linkage Learning in Forest Management.- Performance-Based Computation of Chromosome Lifetimes in Genetic Algorithms.- Genetic Algorithm and Case-Based Reasoning Applied in Production Scheduling.- Knowledge-Based Evolutionary Search for Inductive Concept Learning.- An Evolutionary Algorithm with Tabu Restriction and Heuristic Reasoning for Multiobjective Optimization.- IV Knowledge Incorporation in Fitness Evaluations.- Neural Networks for Fitness Approximation in Evolutionary Optimization.- Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems.- Model Assisted Evolution Strategies.- V Knowledge Incorporation through Life-time Learning and Human-Computer Interactions.- Knowledge Incorporation Through Lifetime Learning.- Local Search Direction for Multi-Objective Optimization Using Memetic EMO Algorithms.- Fashion Design Using Interactive Genetic Algorithm with Knowledge-based Encoding.- Interactive Evolutionary Design.- VI Preference Incorporation in Multi-objective Evolutionary Computation.- Integrating User Preferences into Evolutionary Multi-Objective Optimization.- Human Preferences and their Applications in Evolutionary Multi—Objective Optimization.- An Interactive Fuzzy Satisficing Method for Multi-objective Integer Programming Problems through Genetic Algorithms.- Interactive Preference Incorporation in Evolutionary Engineering Design.

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