Knowledge Incorporation in Evolutionary Computation /
Jin, Yaochu.
Knowledge Incorporation in Evolutionary Computation / edited by Yaochu Jin. - xiii, 548 páginas recurso en línea. - Studies in Fuzziness and Soft Computing, 167 1434-9922 ; .
Springer eBooks
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.
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.
9783540445111
10.1007/9783540445111 doi
TA329-348
Knowledge Incorporation in Evolutionary Computation / edited by Yaochu Jin. - xiii, 548 páginas recurso en línea. - Studies in Fuzziness and Soft Computing, 167 1434-9922 ; .
Springer eBooks
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.
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.
9783540445111
10.1007/9783540445111 doi
TA329-348