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ISBN: 9783540445111
Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu tionary search, into evolutionary a… Mehr…
ISBN: 9783540445111
Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu tionary search, into evolutionary a… Mehr…
ISBN: 9783540445111
Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu- tionary search, into evolutionary … Mehr…
2013, ISBN: 9783540445111
Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu tionary search, into evolutionary … Mehr…
2013, ISBN: 9783540445111
Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu tionary search, into evolutionary … Mehr…
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Detailangaben zum Buch - Knowledge Incorporation in Evolutionary Computation
EAN (ISBN-13): 9783540445111
Erscheinungsjahr: 2013
Herausgeber: Springer Berlin Heidelberg
Buch in der Datenbank seit 2016-01-20T15:40:53+01:00 (Vienna)
Detailseite zuletzt geändert am 2022-11-08T23:16:12+01:00 (Vienna)
ISBN/EAN: 9783540445111
ISBN - alternative Schreibweisen:
978-3-540-44511-1
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: jin, van der linden
Daten vom Verlag:
Autor/in: Yaochu Jin
Titel: Studies in Fuzziness and Soft Computing; Knowledge Incorporation in Evolutionary Computation
Verlag: Springer; Springer Berlin
548 Seiten
Erscheinungsjahr: 2013-04-22
Berlin; Heidelberg; DE
Sprache: Englisch
213,99 € (DE)
220,00 € (AT)
236,00 CHF (CH)
Available
XIII, 550 p.
EA; E107; eBook; Nonbooks, PBS / Mathematik/Sonstiges; Angewandte Mathematik; Verstehen; Case-Based Reasoning; Computer; Evolution Strategies; Evolutionary Computation; Fuzzy; Fuzzy Systems; Genetic Algorithms; Genetic programming; Knowledge Incorporation; Knowledge Representations; human-computer interaction; intelligence; knowledge representation; optimization; programming; C; Applications of Mathematics; Mathematical and Computational Engineering Applications; Artificial Intelligence; Engineering; Mathematik für Ingenieure; Künstliche Intelligenz; BC
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.State of the art in knowledge incorporation in evolutionary computation Comprehensive and self-contained Includes a concise introduction to evolutionary algorithms as well as knowledge representation methods Includes supplementary material: sn.pub/extras
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