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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 Iterative-Deepening Search with On-Line Tree Size Prediction
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    Chapter 2 A Learning Optimization Algorithm in Graph Theory
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    Chapter 3 A Math-Heuristic Dantzig-Wolfe Algorithm for the Capacitated Lot Sizing Problem
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    Chapter 4 Learning and Intelligent Optimization
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    Chapter 5 Parallel Algorithm Configuration
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    Chapter 6 Community Detection in Social and Biological Networks Using Differential Evolution
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    Chapter 7 A Study on Large Population MOEA Using Adaptive ε -Box Dominance and Neighborhood Recombination for Many-Objective Optimization
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    Chapter 8 A Non-adaptive Stochastic Local Search Algorithm for the CHeSC 2011 Competition
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    Chapter 9 Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness
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    Chapter 10 Evaluating Tree-Decomposition Based Algorithms for Answer Set Programming
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    Chapter 11 High-Dimensional Model-Based Optimization Based on Noisy Evaluations of Computer Games
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    Chapter 12 Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling
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    Chapter 13 Minimizing Time When Applying Bootstrap to Contingency Tables Analysis of Genome-Wide Data
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    Chapter 14 Quantifying Homogeneity of Instance Sets for Algorithm Configuration
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    Chapter 15 Automatically Configuring Algorithms for Scaling Performance
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    Chapter 16 Upper Confidence Tree-Based Consistent Reactive Planning Application to MineSweeper
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    Chapter 17 Bounding the Effectiveness of Hypervolume-Based ( μ  +  λ )-Archiving Algorithms
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    Chapter 18 Optimization by ℓ 1 -Constrained Markov Fitness Modelling
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    Chapter 19 Learning and Intelligent Optimization
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    Chapter 20 Quasi-elementary Landscapes and Superpositions of Elementary Landscapes
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    Chapter 21 Fast Permutation Learning
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    Chapter 22 Parameter-Optimized Simulated Annealing for Application Mapping on Networks-on-Chip
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    Chapter 23 Learning Algorithm Portfolios for Parallel Execution
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    Chapter 24 Bayesian Optimization Using Sequential Monte Carlo
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    Chapter 25 Influence of the Migration Period in Parallel Distributed GAs for Dynamic Optimization
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    Chapter 26 A Hyper-Heuristic Inspired by Pearl Hunting
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    Chapter 27 Five Phase and Genetic Hive Hyper-Heuristics for the Cross-Domain Search
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    Chapter 28 Implicit Model Selection Based on Variable Transformations in Estimation of Distribution
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    Chapter 29 Improving the Exploration in Upper Confidence Trees
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    Chapter 30 Parallel GPU Implementation of Iterated Local Search for the Travelling Salesman Problem
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    Chapter 31 Constraint-Based Local Search for the Costas Array Problem
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    Chapter 32 Evaluation of a Family of Reinforcement Learning Cross-Domain Optimization Heuristics
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    Chapter 33 Autonomous Local Search Algorithms with Island Representation
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    Chapter 34 An Approach to Instantly Use Single-Objective Results for Multi-objective Evolutionary Combinatorial Optimization
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    Chapter 35 Lower Bounds and Upper Bounds for MaxSAT
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    Chapter 36 Determining the Characteristic of Difficult Job Shop Scheduling Instances for a Heuristic Solution Method
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    Chapter 37 Expected Improvements for the Asynchronous Parallel Global Optimization of Expensive Functions: Potentials and Challenges
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    Chapter 38 Effect of SMS-EMOA Parameterizations on Hypervolume Decreases
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    Chapter 39 Effects of Speciation on Evolution of Neural Networks in Highly Dynamic Environments
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    Chapter 40 Natural Max-SAT Encoding of Min-SAT
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    Chapter 41 A New Hyperheuristic Algorithm for Cross-Domain Search Problems
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    Chapter 42 Brain Cine-MRI Sequences Registration Using B-Spline Free-Form Deformations and MLSDO Dynamic Optimization Algorithm
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    Chapter 43 Global Optimization for Algebraic Geometry – Computing Runge–Kutta Methods
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    Chapter 44 Clause Sharing in Parallel MaxSAT
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    Chapter 45 An Intelligent Hyper-Heuristic Framework for CHeSC 2011
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    Chapter 46 An Efficient Meta-heuristic Based on Self-control Dominance Concept for a Bi-objective Re-entrant Scheduling Problem with Outsourcing
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    Chapter 47 A Tree Search Approach to Sparse Coding
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    Chapter 48 Adaptive Control of the Number of Crossed Genes in Many-Objective Evolutionary Optimization
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    Chapter 49 Counter Implication Restart for Parallel SAT Solvers
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    Chapter 50 Learning the Neighborhood with the Linkage Tree Genetic Algorithm
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    Chapter 51 A Comparison of Operator Utility Measures for On-Line Operator Selection in Local Search
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    Chapter 52 Monte Carlo Methods for Preference Learning
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    Chapter 53 Hybridizing Reactive Tabu Search with Simulated Annealing
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Title
Learning and Intelligent Optimization
Published by
Springer Berlin Heidelberg, October 2012
DOI 10.1007/978-3-642-34413-8
ISBNs
978-3-64-234412-1, 978-3-64-234413-8
Editors

Hamadi, Youssef, Schoenauer, Marc

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 2%
Portugal 2 2%
Germany 1 1%
United Kingdom 1 1%
Spain 1 1%
Italy 1 1%
Luxembourg 1 1%
United States 1 1%
Sweden 1 1%
Other 0 0%
Unknown 72 87%

Demographic breakdown

Readers by professional status Count As %
Student > Master 18 22%
Student > Ph. D. Student 17 20%
Researcher 15 18%
Professor 7 8%
Student > Doctoral Student 7 8%
Other 19 23%
Readers by discipline Count As %
Computer Science 37 45%
Engineering 19 23%
Unspecified 10 12%
Business, Management and Accounting 5 6%
Mathematics 3 4%
Other 9 11%