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Soft Computing Based Optimization and Decision Models

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Cover of 'Soft Computing Based Optimization and Decision Models'

Table of Contents

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    Book Overview
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    Chapter 1 A Review of Soft Computing Techniques in Maritime Logistics and Its Related Fields
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    Chapter 2 Intelligent Data Analysis, Soft Computing and Imperfect Data
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    Chapter 3 Soft Computing Methods in Transport and Logistics
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    Chapter 4 Applications of Soft Computing in Intelligent Transportation Systems
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    Chapter 5 Fuzzy Cognitive Maps Based Models for Pattern Classification: Advances and Challenges
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    Chapter 6 A Proposal of On-Line Detection of New Faults and Automatic Learning in Fault Diagnosis
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    Chapter 7 Fuzzy Portfolio Selection Models for Dealing with Investor’s Preferences
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    Chapter 8 On Fuzzy Convex Optimization to Portfolio Selection Problem
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    Chapter 9 Digital Coaching for Real Options Support
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    Chapter 10 An Analysis of Decision Criteria for the Selection of Military Training Aircrafts
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    Chapter 11 Participatory Search in Evolutionary Fuzzy Modeling
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    Chapter 12 But, What Is It Actually a Fuzzy Set?
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    Chapter 13 Gradual Numbers and Fuzzy Solutions to Fuzzy Optimization Problems
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    Chapter 14 Using Fuzzy Measures to Construct Multi-criteria Decision Functions
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    Chapter 15 A Modal Account of Preference in a Fuzzy Setting
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    Chapter 16 On Possibilistic Dependencies: A Short Survey of Recent Developments
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    Chapter 17 Penalty Function in Optimization Problems: A Review of Recent Developments
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    Chapter 18 The Single Parameter Family of Gini Bonferroni Welfare Functions and the Binomial Decomposition, Transfer Sensitivity and Positional Transfer Sensitivity
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Title
Soft Computing Based Optimization and Decision Models
Published by
Springer International Publishing, January 2018
DOI 10.1007/978-3-319-64286-4
ISBNs
978-3-31-964285-7, 978-3-31-964286-4
Editors

David A. Pelta, Carlos Cruz Corona

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 15%
Student > Bachelor 4 15%
Professor > Associate Professor 4 15%
Student > Ph. D. Student 4 15%
Researcher 3 11%
Other 5 19%
Unknown 3 11%
Readers by discipline Count As %
Computer Science 8 30%
Engineering 7 26%
Mathematics 2 7%
Business, Management and Accounting 2 7%
Decision Sciences 1 4%
Other 2 7%
Unknown 5 19%