Knowledge Incorporation in Evolutionary Computation

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Jin, Yaochu (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005.
• Σειρά: Studies in Fuzziness and Soft Computing, 167
• Θέματα: Mathematics.; Artificial intelligence.; Applied mathematics.; Engineering mathematics.; Applications of Mathematics.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

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 introd...

Πίνακας περιεχομένων:

• 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.