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Learning and Intelligent Optimization

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Cover of 'Learning and Intelligent Optimization'

Table of Contents

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    Book Overview
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    Chapter 1 Learning a Stopping Criterion for Local Search
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    Chapter 2 Surrogate Assisted Feature Computation for Continuous Problems
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    Chapter 3 MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework
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    Chapter 4 Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers
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    Chapter 5 Extreme Reactive Portfolio (XRP): Tuning an Algorithm Population for Global Optimization
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    Chapter 6 Bounding the Search Space of the Population Harvest Cutting Problem with Multiple Size Stock Selection
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    Chapter 7 Designing and Comparing Multiple Portfolios of Parameter Configurations for Online Algorithm Selection
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    Chapter 8 Portfolios of Subgraph Isomorphism Algorithms
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    Chapter 9 Structure-Preserving Instance Generation
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    Chapter 10 Feature Selection Using Tabu Search with Learning Memory: Learning Tabu Search
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    Chapter 11 The Impact of Automated Algorithm Configuration on the Scaling Behaviour of State-of-the-Art Inexact TSP Solvers
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    Chapter 12 Requests Management for Smartphone-Based Matching Applications Using a Multi-agent Approach
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    Chapter 13 Self-organizing Neural Network for Adaptive Operator Selection in Evolutionary Search
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    Chapter 14 Quantifying the Similarity of Algorithm Configurations
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    Chapter 15 Neighborhood Synthesis from an Ensemble of MIP and CP Models
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    Chapter 16 Parallelizing Constraint Solvers for Hard RCPSP Instances
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    Chapter 17 Characterization of Neighborhood Behaviours in a Multi-neighborhood Local Search Algorithm
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    Chapter 18 Constraint Programming and Machine Learning for Interactive Soccer Analysis
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    Chapter 19 A Matheuristic Approach for the p -Cable Trench Problem
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    Chapter 20 An Empirical Study of Per-instance Algorithm Scheduling
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    Chapter 21 Dynamic Strategy to Diversify Search Using a History Map in Parallel Solving
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    Chapter 22 Faster Model-Based Optimization Through Resource-Aware Scheduling Strategies
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    Chapter 23 Risk-Averse Anticipation for Dynamic Vehicle Routing
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    Chapter 24 Solving GENOPT Problems with the Use of ExaMin Solver
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    Chapter 25 Hybridisation of Evolutionary Algorithms Through Hyper-heuristics for Global Continuous Optimisation
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Mentioned by

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1 tweeter
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1 Wikipedia page

Citations

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2 Dimensions

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Title
Learning and Intelligent Optimization
Published by
Springer International Publishing, November 2016
DOI 10.1007/978-3-319-50349-3
ISBNs
978-3-31-950348-6, 978-3-31-950349-3
Editors

Festa, Paola, Sellmann, Meinolf, Vanschoren, Joaquin

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 60%
Student > Master 2 40%
Readers by discipline Count As %
Computer Science 4 80%
Engineering 1 20%