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Band 167

Knowledge Incorporation in Evolutionary Computation

Aus der Reihe Studies in Fuzziness and Soft Computing

197,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

18.12.2010

Abbildungen

XIII, 548 p.

Herausgeber

Yaochu Jin

Verlag

Springer Berlin

Seitenzahl

548

Maße (L/B/H)

23,5/15,5/3,1 cm

Gewicht

844 g

Auflage

2005

Sprache

Englisch

ISBN

978-3-642-06174-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

18.12.2010

Abbildungen

XIII, 548 p.

Herausgeber

Yaochu Jin

Verlag

Springer Berlin

Seitenzahl

548

Maße (L/B/H)

23,5/15,5/3,1 cm

Gewicht

844 g

Auflage

2005

Sprache

Englisch

ISBN

978-3-642-06174-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Knowledge Incorporation in Evolutionary Computation
  • Produktbild: 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 Interactive Fuzzy Satisficing Method for Multi-objective Integer Programming Problems through Genetic Algorithms.- Interactive Preference Incorporation in Evolutionary Engineering Design.