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 Lovász 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 Viaa 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 Pointsin 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.

This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Conference on Learning and Intelligent Optimization, LION 12, held in Kalamata, Greece, in June 2018. The 28 full papers and 12 short papers presented have been carefully reviewed and selected from 62 submissions. The papers explore the advanced research developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence. Special focus is given to advanced ideas, technologies, methods, and applications in optimization and machine learning.