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04557nam a22004815i 4500 |
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|a 9783540445111
|9 978-3-540-44511-1
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|a 10.1007/978-3-540-44511-1
|2 doi
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|a T57-57.97
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|a PBW
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|a MAT003000
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|a 519
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|a Knowledge Incorporation in Evolutionary Computation
|h [electronic resource] /
|c edited by Yaochu Jin.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2005.
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|a XIII, 550 p.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
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|a text file
|b PDF
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|a Studies in Fuzziness and Soft Computing,
|x 1434-9922 ;
|v 167
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|a 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.
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|a 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.
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650 |
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|a Mathematics.
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|a Artificial intelligence.
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|a Applied mathematics.
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|a Engineering mathematics.
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|a Mathematics.
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|a Applications of Mathematics.
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|a Appl.Mathematics/Computational Methods of Engineering.
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650 |
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|a Artificial Intelligence (incl. Robotics).
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700 |
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|a Jin, Yaochu.
|e editor.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783642061745
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|a Studies in Fuzziness and Soft Computing,
|x 1434-9922 ;
|v 167
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856 |
4 |
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|u http://dx.doi.org/10.1007/978-3-540-44511-1
|z Full Text via HEAL-Link
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912 |
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|a ZDB-2-ENG
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950 |
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|a Engineering (Springer-11647)
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