Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence
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Published By Springer Berlin Heidelberg

9783642002663, 9783642002670

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Differential Evolution with Fitness Diversity Self-adaptation

Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence ◽

10.1007/978-3-642-00267-0_7 ◽

2009 ◽

pp. 199-234 ◽

Cited By ~ 18

Author(s):

Ville Tirronen ◽

Ferrante Neri

Keyword(s):

Differential Evolution ◽

Self Adaptation

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The Evolutionary-Gradient-Search Procedure in Theory and Practice

Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence ◽

10.1007/978-3-642-00267-0_3 ◽

2009 ◽

pp. 77-101 ◽

Cited By ~ 2

Author(s):

Ralf Salomon ◽

Dirk V. Arnold

Keyword(s):

Theory And Practice ◽

Search Procedure ◽

Gradient Search

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Evolutionary Optimization for Multiobjective Portfolio Selection under Markowitz’s Model with Application to the Caracas Stock Exchange

Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence ◽

10.1007/978-3-642-00267-0_18 ◽

2009 ◽

pp. 489-509 ◽

Cited By ~ 3

Author(s):

Feijoo Colomine Duran ◽

Carlos Cotta ◽

Antonio J. Fernández

Keyword(s):

Portfolio Selection ◽

Stock Exchange ◽

Evolutionary Optimization

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Applying River Formation Dynamics to Solve NP-Complete Problems

Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence ◽

10.1007/978-3-642-00267-0_12 ◽

2009 ◽

pp. 333-368 ◽

Cited By ~ 14

Author(s):

Pablo Rabanal ◽

Ismael Rodríguez ◽

Fernando Rubio

Keyword(s):

Complete Problems ◽

Formation Dynamics ◽

River Formation Dynamics ◽

Np Complete

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Algorithms Inspired in Social Phenomena

Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence ◽

10.1007/978-3-642-00267-0_13 ◽

2009 ◽

pp. 369-387 ◽

Cited By ~ 3

Author(s):

Antonio Neme ◽

Sergio Hernández

Keyword(s):

Social Phenomena

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Fish School Search

Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence ◽

10.1007/978-3-642-00267-0_9 ◽

2009 ◽

pp. 261-277 ◽

Cited By ~ 39

Author(s):

Carmelo J. A. Bastos Filho ◽

Fernando B. de Lima Neto ◽

Anthony J. C. C. Lins ◽

Antônio I. S. Nascimento ◽

Marília P. Lima

Keyword(s):

Fish School

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Central Pattern Generators: Optimisation and Application

Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence ◽

10.1007/978-3-642-00267-0_8 ◽

2009 ◽

pp. 235-260 ◽

Cited By ~ 2

Author(s):

Leena N. Patel

Keyword(s):

Central Pattern Generators ◽

Pattern Generators

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The Rationale Behind Seeking Inspiration from Nature

Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence ◽

10.1007/978-3-642-00267-0_2 ◽

2009 ◽

pp. 51-76 ◽

Cited By ~ 7

Author(s):

Kent C. B. Steer ◽

Andrew Wirth ◽

Saman K. Halgamuge

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A Model-Assisted Memetic Algorithm for Expensive Optimization Problems

Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence ◽

10.1007/978-3-642-00267-0_5 ◽

2009 ◽

pp. 133-169 ◽

Cited By ~ 5

Author(s):

Yoel Tenne

Keyword(s):

Memetic Algorithm ◽

Optimization Problems ◽

Expensive Optimization Problems ◽

Expensive Optimization

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The Evolutionary Transition Algorithm: Evolving Complex Solutions Out of Simpler Ones

Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence ◽

10.1007/978-3-642-00267-0_4 ◽

2009 ◽

pp. 103-131

Author(s):

Tom Lenaerts ◽

Anne Defaweux ◽

Jano van Hemert

Keyword(s):

Evolutionary Transition ◽

Complex Solutions

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Nature-Inspired Algorithms for Optimisation - Studies in Computational IntelligenceLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer Berlin Heidelberg

Differential Evolution with Fitness Diversity Self-adaptation

The Evolutionary-Gradient-Search Procedure in Theory and Practice

Evolutionary Optimization for Multiobjective Portfolio Selection under Markowitz’s Model with Application to the Caracas Stock Exchange

Applying River Formation Dynamics to Solve NP-Complete Problems

Algorithms Inspired in Social Phenomena

Fish School Search

Central Pattern Generators: Optimisation and Application

The Rationale Behind Seeking Inspiration from Nature

A Model-Assisted Memetic Algorithm for Expensive Optimization Problems

The Evolutionary Transition Algorithm: Evolving Complex Solutions Out of Simpler Ones

Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence
Latest Publications