Learning and intelligent optimization

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Inhaltsverzeichnis Main Track (Regular Papers) includes a range of innovative algorithms addressing various optimization challenges. Topics cover a heuristic for the General Vehicle Routing Problem, a combination of evolutionary algorithms and mathematical programming for Just-In-Time Job-Shop Scheduling, and a math-heuristic for DNA sequencing. Additionally, a randomized iterated greedy algorithm tackles the Founder Sequence Reconstruction Problem, while adaptive methods are explored in two-phase local search and filter SQP. The section also discusses algorithm selection as a bandit problem and presents rigorous runtime analysis of bandit-based estimation of distribution algorithms for noisy optimization. Other contributions include consistency modifications for Monte-Carlo Tree Search, distance functions in microarray data analysis, and Gaussian process-assisted particle swarm optimization. The Main Track (Short Papers) features a linear approximation for dynamic programming in ship scheduling, a multilevel scheme for multiway graph partitioning, and a network approach for restructuring the Korean freight railway. Other highlights include a parallel multi-objective evolutionary algorithm for phylogenetic inference, convergence studies of probability collectives, and generative topographic mapping for dimension reduction. The section also covers decision tree learning for optimization heuristics, multidisciplinary design opt