Evolutionary Computation in Combinatorial Optimization 25th European Conference, EvoCOP 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23–25, 2025, Proceedings

This book constitutes the referred proceedings of the 25th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2025, held as part of EvoStar 2025, in Trieste, Italy, during April 23–25, 2025.
The 16 full papers presented in this book were carefully reviewed and selected from 43 submissions. These papers cover a variety of topics, ranging from benchmark creation, over genetic programming, heuristics for real-world and NP-hard problems, as well as the foundations of evolutionary computation algorithms and other search heuristics, to both mixed-binary and multi-objective optimization.

.- A Runtime Analysis of the Multi-Valued Compact Genetic Algorithm on Generalized LeadingOnes.
.- Evolutionary Anytime Algorithms.
.- Studies on Survival Strategies to Protect Expert Knowledge in Evolutionary Algorithms for Interactive Role Mining.
.- Diversification through Candidate Sampling for a Non-Iterated Lin-Kernighan-Helsgaun Algorithm.
.- Instance Space Analysis and Algorithm Selection for a Parallel Batch Scheduling Problem.
.- Meta-learning of Univariate Estimation-of-Distribution Algorithms for Pseudo-Boolean Problems.
.- A Selective Vehicle Routing Problem for the Bloodmobile System.
.- A Genetic Approach to the Operational Freight-on-Transit problem.
.- LON/D — Sub-problem Landscape Analysis in Decomposition-based Multi-objective Optimization.
.- Visualizing Pseudo-Boolean Functions: Feature Selection and Regularization for Machine Learning.
.- Mixed-Binary Problems Optimized with Fast Discrete Solver.
.- Feature-based Evolutionary Diversity Optimization of Discriminating Instances for Chance-constrained Optimization Problems.
.- Adaptive neighborhood search based on landscape learning: a TSP study.
.- Healthcare Facility Location Problem and Fitness Landscape Analysis.
.- Generating (Semi-)Active Schedules for Dynamic Multi-mode Project Scheduling Using Genetic Programming Hyper-heuristics.
.- Price-and-branch Heuristic for Vector Bin Packing.