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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 A Column Generation Heuristic for the General Vehicle Routing Problem
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    Chapter 2 A Combination of Evolutionary Algorithm, Mathematical Programming, and a New Local Search Procedure for the Just-In-Time Job-Shop Scheduling Problem
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    Chapter 3 A Math-Heuristic Algorithm for the DNA Sequencing Problem
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    Chapter 4 A Randomized Iterated Greedy Algorithm for the Founder Sequence Reconstruction Problem
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    Chapter 5 Adaptive “Anytime” Two-Phase Local Search
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    Chapter 6 Adaptive Filter SQP
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    Chapter 7 Algorithm Selection as a Bandit Problem with Unbounded Losses
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    Chapter 8 Bandit-Based Estimation of Distribution Algorithms for Noisy Optimization: Rigorous Runtime Analysis
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    Chapter 9 Consistency Modifications for Automatically Tuned Monte-Carlo Tree Search
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    Chapter 10 Distance Functions, Clustering Algorithms and Microarray Data Analysis
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    Chapter 11 Gaussian Process Assisted Particle Swarm Optimization
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    Chapter 12 Learning of Highly-Filtered Data Manifold Using Spectral Methods
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    Chapter 13 Multiclass Visual Classifier Based on Bipartite Graph Representation of Decision Tables
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    Chapter 14 A Linear Approximation of the Value Function of an Approximate Dynamic Programming Approach for the Ship Scheduling Problem
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    Chapter 15 A Multilevel Scheme with Adaptive Memory Strategy for Multiway Graph Partitioning
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    Chapter 16 A Network Approach for Restructuring the Korean Freight Railway Considering Customer Behavior
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    Chapter 17 A Parallel Multi-Objective Evolutionary Algorithm for Phylogenetic Inference
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    Chapter 18 Learning and Intelligent Optimization
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    Chapter 19 Generative Topographic Mapping for Dimension Reduction in Engineering Design
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    Chapter 20 Learning Decision Trees for the Analysis of Optimization Heuristics
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    Chapter 21 On the Coordination of Multidisciplinary Design Optimization Using Expert Systems
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    Chapter 22 On the Potentials of Parallelizing Large Neighbourhood Search for Rich Vehicle Routing Problems
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    Chapter 23 Optimized Ensembles for Clustering Noisy Data
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    Chapter 24 Stochastic Local Search for the Optimization of Secondary Structure Packing in Proteins
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    Chapter 25 Systematic Improvement of Monte-Carlo Tree Search with Self-generated Neural-Networks Controllers
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    Chapter 26 Grapheur: A Software Architecture for Reactive and Interactive Optimization
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    Chapter 27 The EvA2 Optimization Framework
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    Chapter 28 Feature Extraction from Optimization Data via DataModeler’s Ensemble Symbolic Regression
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    Chapter 29 Understanding TSP Difficulty by Learning from Evolved Instances
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    Chapter 30 Time-Bounded Sequential Parameter Optimization
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    Chapter 31 Pitfalls in Instance Generation for Udine Timetabling
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    Chapter 32 A Study of the Parallelization of the Multi-Objective Metaheuristic MOEA/D
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    Chapter 33 An Interactive Evolutionary Multi-objective Optimization Method Based on Polyhedral Cones
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    Chapter 34 On the Distribution of EMOA Hypervolumes
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    Chapter 35 Adapting to a Realistic Decision Maker: Experiments towards a Reactive Multi-objective Optimizer
Attention for Chapter 29: Understanding TSP Difficulty by Learning from Evolved Instances
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Chapter title
Understanding TSP Difficulty by Learning from Evolved Instances
Chapter number 29
Book title
Learning and Intelligent Optimization
Published by
Springer, Berlin, Heidelberg, January 2010
DOI 10.1007/978-3-642-13800-3_29
Book ISBNs
978-3-64-213799-0, 978-3-64-213800-3
Authors

Kate Smith-Miles, Jano van Hemert, Xin Yu Lim, Smith-Miles, Kate, Hemert, Jano van, Lim, Xin Yu

X Demographics

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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Czechia 1 3%
Germany 1 3%
Brazil 1 3%
Unknown 33 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 42%
Student > Bachelor 5 14%
Student > Master 5 14%
Researcher 5 14%
Student > Doctoral Student 1 3%
Other 4 11%
Unknown 1 3%
Readers by discipline Count As %
Computer Science 22 61%
Business, Management and Accounting 3 8%
Mathematics 2 6%
Engineering 2 6%
Decision Sciences 1 3%
Other 1 3%
Unknown 5 14%