Learning and intelligent optimizatio...
LION (Conference) (2018 :)

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  • Learning and intelligent optimization = 12th International Conference, LION 12, Kalamata, Greece, June 10-15, 2018 : revised selected papers /
  • 紀錄類型: 書目-電子資源 : Monograph/item
    正題名/作者: Learning and intelligent optimization/ edited by Roberto Battiti ... [et al.].
    其他題名: 12th International Conference, LION 12, Kalamata, Greece, June 10-15, 2018 : revised selected papers /
    其他題名: LION 12
    其他作者: Battiti, Roberto.
    團體作者: LION (Conference)
    出版者: Cham :Springer International Publishing : : 2019.,
    面頁冊數: xii, 474 p. :ill. (some col.), digital ;24 cm.
    內容註: Accelerated Randomized Coordinate Descent Algorithms for Stochastic Optimization and Online Learning -- An Improved BTK Algorithm Based on Cell-like P System with Active Membranes -- A Simple Algorithmic Proof of the Symmetric Lopsided Lovasz Local Lemma -- Creating a Multi-Iterative-Priority-Rule for the Job Shop Scheduling Problem with Focus on Tardy Jobs via Genetic Programming -- A Global Optimization Algorithm for Non-Convex Mixed-Integer Problems -- Massive 2-opt and 3-opt Moves with High Performance GPU Local Search to Large-scale Traveling Salesman Problem -- Instance-Specific Selection of AOS Methods for Solving Combinatorial Optimization Problems via Neural Networks -- CAVE: Configuration Assessment, Visualization and Evaluation -- The Accuracy of One Polynomial Algorithm for the Convergecast Scheduling Problem on a Square Grid with Rectangular Obstacles -- An Effective Heuristic for a Single-Machine Scheduling Problem with Family Setups and Resource Constraints -- Learning the Quality of Dispatch Heuristics Generated by Automated Programming -- Explaining Heuristic Performance Differences for Vehicle Routing Problems with Time Windows -- Targeting Well-Balanced Solutions in Multi-Objective Bayesian Optimization under a Restricted Budget -- How Grossone Can Be Helpful to Iteratively Compute Negative Curvature Directions -- Solving Scalarized Subproblems Within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems -- Exact and Heuristic Approaches for the Longest Common Palindromic Subsequence Problem -- Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time -- Algorithm Configuration: Learning Policies for the Quick Termination of Poor Performers -- Probability Estimation by An Adapted Genetic Algorithm in Web Insurance -- Adaptive Multi-Objective Local Search Algorithms for the Permutation Flowshop Scheduling Problem -- Portfolio Optimization Via a Surrogate Risk Measure: Conditional Desirability Value at Risk (CDVaR) -- Rover Descent: Learning to Optimize by Learning to Navigate on Prototypical Loss Surfaces -- Analysis of Algorithm Components and Parameters: Some Case Studies -- Optimality of Multiple Decision Statistical Procedure for Gaussian Graphical : Model Selection -- Hyper-Reactive Tabu Search for MaxSAT -- Exact Algorithms for Two Quadratic Euclidean Problems of Searching for the Largest Subset and Longest Subsequence -- A Restarting Rule Based on the Schnabel Census for Genetic Algorithms -- Intelligent Pump Scheduling Optimization in Water Distribution Networks Detecting Patterns in Benchmark Instances of the Swap-body Vehicle Routing Problem -- Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities -- Asymptotically Optimal Algorithm for the Maximum m-Peripatetic Salesman Problem in a Normed Space -- Computational Intelligence for Locating Garbage Accumulation Points in Urban Scenarios -- Fully Convolutional Neural Networks for Mapping Oil Palm Plantations in Kalimantan -- Calibration of a Water Distribution Network with Limited Field Measures: the Case Study of Castellammare di Stabia (Naples, Italy) -- Combinatorial Methods for Testing Communication Protocols in Smart Cities -- Pseudo-pyramidal Tours and Efficient Solvability of the Euclidean Generalized Traveling Salesman Problem in Grid Clusters -- Constant Factor Approximation for Intersecting Line Segments with Disks -- Scheduling Deteriorating Jobs and Module Changes with Incompatible Job Families on Parallel Machines Using a Hybrid SADE-AFSA Algorithm.
    Contained By: Springer eBooks
    標題: Machine learning - Congresses. -
    電子資源: https://doi.org/10.1007/978-3-030-05348-2
    ISBN: 9783030053482
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