Knowledge Incorporation in Evolutionary Computation (eBook, PDF)

Redaktion: Jin, Yaochu

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 548
• Erscheinungstermin: 22. April 2013
• Englisch
• ISBN-13: 9783540445111
• Artikelnr.: 44129521

Inhaltsangabe

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.