Main Track (Regular Papers) -- A Column Generation Heuristic for the General Vehicle Routing Problem -- A Combination of Evolutionary Algorithm, Mathematical Programming, and a New Local Search Procedure for the Just-In-Time Job-Shop Scheduling Problem -- A Math-Heuristic Algorithm for the DNA Sequencing Problem -- A Randomized Iterated Greedy Algorithm for the Founder Sequence Reconstruction Problem -- Adaptive “Anytime” Two-Phase Local Search -- Adaptive Filter SQP -- Algorithm Selection as a Bandit Problem with Unbounded Losses -- Bandit-Based Estimation of Distribution Algorithms for Noisy Optimization: Rigorous Runtime Analysis -- Consistency Modifications for Automatically Tuned Monte-Carlo Tree Search -- Distance Functions, Clustering Algorithms and Microarray Data Analysis -- Gaussian Process Assisted Particle Swarm Optimization -- Learning of Highly-Filtered Data Manifold Using Spectral Methods -- Multiclass Visual Classifier Based on Bipartite Graph Representation of Decision Tables -- Main Track (Short Papers) -- A Linear Approximation of the Value Function of an Approximate Dynamic Programming Approach for the Ship Scheduling Problem -- A Multilevel Scheme with Adaptive Memory Strategy for Multiway Graph Partitioning -- A Network Approach for Restructuring the Korean Freight Railway Considering Customer Behavior -- A Parallel Multi-Objective Evolutionary Algorithm for Phylogenetic Inference -- Convergence of Probability Collectives with Adaptive Choice of Temperature Parameters -- Generative Topographic Mapping for Dimension Reduction in Engineering Design -- Learning Decision Trees for the Analysis of Optimization Heuristics -- On the Coordination of Multidisciplinary Design Optimization Using Expert Systems -- On the Potentials of Parallelizing Large Neighbourhood Search for Rich Vehicle Routing Problems -- Optimized Ensembles for Clustering Noisy Data -- Stochastic Local Search for the Optimization of Secondary Structure Packing in Proteins -- Systematic Improvement of Monte-Carlo Tree Search with Self-generated Neural-Networks Controllers -- Special Session: LION-SWOP -- Grapheur: A Software Architecture for Reactive and Interactive Optimization -- The EvA2 Optimization Framework -- Special Session: LION-CCEC -- Feature Extraction from Optimization Data via DataModeler’s Ensemble Symbolic Regression -- Special Session: LION-PP -- Understanding TSP Difficulty by Learning from Evolved Instances -- Time-Bounded Sequential Parameter Optimization -- Pitfalls in Instance Generation for Udine Timetabling -- Special Session: LION-MOME -- A Study of the Parallelization of the Multi-Objective Metaheuristic MOEA/D -- An Interactive Evolutionary Multi-objective Optimization Method Based on Polyhedral Cones -- On the Distribution of EMOA Hypervolumes -- Adapting to a Realistic Decision Maker: Experiments towards a Reactive Multi-objective Optimizer.

This book constitutes the thoroughly refereed proceedings of the 4th International Conference on Learning and Intelligent Optimization, LION 4, held in Venice, Italy, in January 2010. The 23 regular and 12 short papers were carefully reviewed and selected from 87 submissions. Topics covered include metaheuristics (tabu search, iterated local search, evolutionary algorithms, memetic algorithms, ant colony optmization, particle swarm optimization); hybridizations of metaheuristics with other techniques for optimization; supervised, unsupervised and reinforcement learning applied to heuristic search, reactive search optimization; self-adaptive algorithms; hyperheuristics; algorithms for dynamic, stochastic and multi-objective problems; interfaces between discrete and continuous optimization; experimental analysis and modeling of algorithms; parallelization of optimization algorithms; memory-based optimization; and software engineering of learning and intelligent optimization methods.