Evolutionary Computation in Combinatorial Optimization

This book constitutes the referred proceedings of the 24th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2024, held as part of EvoStar 2024, in Aberystwyth, UK, during April 35, 2024.

The 12 full papers presented in this book were carefully reviewed and selected from 28 submissions. They cover a variety of topics, ranging from constructive algorithms, machine learning techniques ranging from neural network based guidance to sparse surrogate models for optimization problems, the foundation of evolutionary computation algorithms and other search heuristics, to multi-objective optimization problems.

Inhalt

A Neural Network Based Guidance for a BRKGA: An Application to the Longest Common Square Subsequence Problem.- Sparse Surrogate Model for Optimization: Example of the Bus Stops Spacing Problem.- Emergence of new local search algorithms with neuro-evolution.- Q-learning Based Framework for Solving the Stochastic E-waste Collection Problem.- A memetic algorithm with adaptive operator selection for graph coloring.- Studies on Multi-objective Role Mining in ERP Systems.- Greedy heuristic guided by lexicographic excellence.- Reduction-Based MAX-3SAT with Low Nonlinearity and Lattices Under Recombination.- Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances.- Hardest Monotone Functions for Evolutionary Algorithms.- A Theoretical Investigation Of Termination Criteria For Evolutionary Algorithms.- Experimental and Theoretical Analysis of Local Search Optimising OBDD Variable Orderings.