Learning and Intelligent Optimization

Table of Contents

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   Book Overview
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   Chapter 1 Accelerated Randomized Coordinate Descent Algorithms for Stochastic Optimization and Online Learning
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   Chapter 2 Spiral Search Method to GPU Parallel Euclidean Minimum Spanning Tree Problem
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   Chapter 3 A Cooperative Learning Approach for the Quadratic Knapsack Problem
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   Chapter 4 An Improved BTK Algorithm Based on Cell-Like P System with Active Membranes
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   Chapter 5 A Simple Algorithmic Proof of the Symmetric Lopsided Lovász Local Lemma
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   Chapter 6 Creating a Multi-iterative-Priority-Rule for the Job Shop Scheduling Problem with Focus on Tardy Jobs via Genetic Programming
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   Chapter 7 A Global Optimization Algorithm for Non-Convex Mixed-Integer Problems
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   Chapter 8 Massive 2-opt and 3-opt Moves with High Performance GPU Local Search to Large-Scale Traveling Salesman Problem
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    Chapter 9 Instance-Specific Selection of AOS Methods for Solving Combinatorial Optimisation Problems via Neural Networks
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    Chapter 10 CAVE: Configuration Assessment, Visualization and Evaluation
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    Chapter 11 The Accuracy of One Polynomial Algorithm for the Convergecast Scheduling Problem on a Square Grid with Rectangular Obstacles
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    Chapter 12 An Effective Heuristic for a Single-Machine Scheduling Problem with Family Setups and Resource Constraints
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    Chapter 13 Learning the Quality of Dispatch Heuristics Generated by Automated Programming
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    Chapter 14 Explaining Heuristic Performance Differences for Vehicle Routing Problems with Time windows
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    Chapter 15 Targeting Well-Balanced Solutions in Multi-Objective Bayesian Optimization Under a Restricted Budget
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    Chapter 16 How Grossone Can Be Helpful to Iteratively Compute Negative Curvature Directions
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    Chapter 17 Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems
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    Chapter 18 Exact and Heuristic Approaches for the Longest Common Palindromic Subsequence Problem
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    Chapter 19 Multi-objective Performance Measurement: Alternatives to PAR10 and Expected Running Time
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    Chapter 20 Algorithm Configuration: Learning Policies for the Quick Termination of Poor Performers
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    Chapter 21 Probability Estimation by an Adapted Genetic Algorithm in Web Insurance
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    Chapter 22 Adaptive Multi-objective Local Search Algorithms for the Permutation Flowshop Scheduling Problem
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    Chapter 23 Portfolio Optimization via a Surrogate Risk Measure: Conditional Desirability Value at Risk (CDVaR)
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    Chapter 24 Rover Descent: Learning to Optimize by Learning to Navigate on Prototypical Loss Surfaces
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    Chapter 25 Analysis of Algorithm Components and Parameters: Some Case Studies
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    Chapter 26 Optimality of Multiple Decision Statistical Procedure for Gaussian Graphical Model Selection
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    Chapter 27 Hyper-Reactive Tabu Search for MaxSAT
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    Chapter 28 Exact Algorithms for Two Quadratic Euclidean Problems of Searching for the Largest Subset and Longest Subsequence
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    Chapter 29 A Restarting Rule Based on the Schnabel Census for Genetic Algorithms
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    Chapter 30 Intelligent Pump Scheduling Optimization in Water Distribution Networks
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    Chapter 31 Detecting Patterns in Benchmark Instances of the Swap-Body Vehicle Routing Problem
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    Chapter 32 Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities
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    Chapter 33 Asymptotically Optimal Algorithm for the Maximum m -Peripatetic Salesman Problem in a Normed Space
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    Chapter 34 Computational Intelligence for Locating Garbage Accumulation Points in Urban Scenarios
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    Chapter 35 Fully Convolutional Neural Networks for Mapping Oil Palm Plantations in Kalimantan
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    Chapter 36 Calibration of a Water Distribution Network with Limited Field Measures: The Case Study of Castellammare di Stabia (Naples, Italy)
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    Chapter 37 Combinatorial Methods for Testing Communication Protocols in Smart Cities
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    Chapter 38 Pseudo-pyramidal Tours and Efficient Solvability of the Euclidean Generalized Traveling Salesman Problem in Grid Clusters
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    Chapter 39 Constant Factor Approximation for Intersecting Line Segments with Disks
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    Chapter 40 Scheduling Deteriorating Jobs and Module Changes with Incompatible Job Families on Parallel Machines Using a Hybrid SADE-AFSA Algorithm