Knowledge Incorporation in Evolutionary Computation

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