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Learning and Intelligent Optimization

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Table of Contents

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    Book Overview
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    Chapter 1 An Importance Sampling Approach to the Estimation of Algorithm Performance in Automated Algorithm Design
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    Chapter 2 Test Problems for Parallel Algorithms of Constrained Global Optimization
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    Chapter 3 Automatic Configuration of Kernel-Based Clustering: An Optimization Approach
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    Chapter 4 Solution of the Convergecast Scheduling Problem on a Square Unit Grid When the Transmission Range is 2
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    Chapter 5 A GRASP for the Minimum Cost SAT Problem
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    Chapter 6 A New Local Search for the p-Center Problem Based on the Critical Vertex Concept
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    Chapter 7 An Iterated Local Search Framework with Adaptive Operator Selection for Nurse Rostering
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    Chapter 8 Learning a Reactive Restart Strategy to Improve Stochastic Search
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    Chapter 9 Efficient Adaptive Implementation of the Serial Schedule Generation Scheme Using Preprocessing and Bloom Filters
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    Chapter 10 Interior Point and Newton Methods in Solving High Dimensional Flow Distribution Problems for Pipe Networks
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    Chapter 11 Hierarchical Clustering and Multilevel Refinement for the Bike-Sharing Station Planning Problem
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    Chapter 12 Decomposition Descent Method for Limit Optimization Problems
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    Chapter 13 RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization
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    Chapter 14 A New Constructive Heuristic for the No-Wait Flowshop Scheduling Problem
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    Chapter 15 Sharp Penalty Mappings for Variational Inequality Problems
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    Chapter 16 A Nonconvex Optimization Approach to Quadratic Bilevel Problems
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    Chapter 17 An Experimental Study of Adaptive Capping in irace
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    Chapter 18 Duality Gap Analysis of Weak Relaxed Greedy Algorithms
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    Chapter 19 Controlling Some Statistical Properties of Business Rules Programs
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    Chapter 20 Hybridization and Discretization Techniques to Speed Up Genetic Algorithm and Solve GENOPT Problems
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    Chapter 21 Identification of Discontinuous Thermal Conductivity Coefficient Using Fast Automatic Differentiation
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    Chapter 22 Comparing Two Approaches for Solving Constrained Global Optimization Problems
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    Chapter 23 Towards a Universal Modeller of Chaotic Systems
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    Chapter 24 An Approach for Generating Test Problems of Constrained Global Optimization
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    Chapter 25 Global Optimization Using Numerical Approximations of Derivatives
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    Chapter 26 Global Optimization Challenges in Structured Low Rank Approximation
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    Chapter 27 A D.C. Programming Approach to Fractional Problems
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    Chapter 28 Objective Function Decomposition in Global Optimization
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    Chapter 29 Projection Approach Versus Gradient Descent for Network’s Flows Assignment Problem
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    Chapter 30 An Approximation Algorithm for Preemptive Speed Scaling Scheduling of Parallel Jobs with Migration
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    Chapter 31 Learning and Intelligent Optimization for Material Design Innovation
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    Chapter 32 Statistical Estimation in Global Random Search Algorithms in Case of Large Dimensions
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    Chapter 33 A Model of FPGA Massively Parallel Calculations for Hard Problem of Scheduling in Transportation Systems
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    Chapter 34 Accelerating Gradient Descent with Projective Response Surface Methodology
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    Chapter 35 Emmental-Type GKLS-Based Multiextremal Smooth Test Problems with Non-linear Constraints
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Title
Learning and Intelligent Optimization
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-69404-7
ISBNs
978-3-31-969403-0, 978-3-31-969404-7
Editors

Roberto Battiti, Dmitri E. Kvasov, Yaroslav D. Sergeyev

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Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Engineering 1 100%