Knowledge incorporation in evolutionary computation

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...

Deskribapen osoa

Gorde:
Xehetasun bibliografikoak
Egile nagusia: Jin, Yaochu, 1966-
Beste egile batzuk: Jin, Yaochu (Argitalpenaren zuzendaria)
Formatua: Livre numérique
Hizkuntza:Anglais
Argitaratua: Berlin, Heidelberg : Springer Berlin Heidelberg 2005.
Cham : Springer Nature
Saila:Studies in Fuzziness and Soft Computing 167
Sarrera elektronikoa:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Oharra: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Knowledge Incorporation in Evolutionary Computation, Texte imprimé, 9783642061745
• Knowledge Incorporation in Evolutionary Computation, Texte imprimé, 9783540229025
• Knowledge Incorporation in Evolutionary Computation, Texte imprimé, 9783642535970
Aurkibidea:
  • 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.