LION 4, the 4th International Conference on Learning and Intelligent Op- mizatioN, was held during January 18-22 in Venice, Italy. This meeting, which continues the successful series of LION conferences, was aimed at exploring the intersectionsand uncharted territoriesbetween machine learning,arti?cial int- ligence, mathematical programming and algorithms for hard optimization pr- lems. The main purpose of the event was to bring together experts from these areas to discuss new ideas and methods, challenges and opportunities in various application areas, general trends and speci?c developments. Despite the economical crisis in 2009, which visibly a?ected the submission numbers of many conferences and workshops, we received an impressive n- ber of 87 submissions. As in previous years, we o?ered three di?erent paper categories for submission: (1) regular papers on original and unpublished work, (2)shortpapersonoriginalandunpublishedwork,and(3)worksfororalpres- tation only. Accepted papers from the ?rst two categories are published in the proceedings. Fromthe 87 submissions that wereceived,57 fell into the ?rstca- gory, 22 into the second one, and 8 into the last one.
After a thorough reviewing process we accepted 19 regular papers (which amounts to an acceptance rate of 33%) and 11 short papers for publication in the proceedings. Additionally, nine regular paperswere accepted as shortpapers, and all eight submissions from the third category were accepted for presentation.
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.