Evolutionary Computation in Combinatorial Optimization
Description
The 15 revised full papers presented in this book were carefully reviewed and selected from 32 submissions. They present recent theoretical and experimental advances in combinatorial optimization, evolutionary algorithms, and related research fields.
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Content
Fairer comparisons for Travelling Salesman Problem solutions using Hash Functions.- Application of Adapt-CMSA to the Two-Echelon Electric Vehicle Routing Problem with Simultaneous Pickup and Deliveries.- Real-World Vehicle Routing using Adaptive Large Neighborhood Search.- A Multilevel Optimization Approach for Large Scale Battery Exchange Station Location Planning.- A memetic algorithm for deinterleaving pulse trains.- Application of Negative Learning Ant Colony Optimization to the Far From Most String Problem.- Monte Carlo Tree Search with Adaptive Simulation: a Case Study on Weighted Vertex Coloring.- Evolutionary Strategies for the Design of Binary Linear Codes.- A Policy-Based Learning Beam Search for Combinatorial Optimization.- Cooperative Coevolutionary Genetic Programming Hyper Heuristic for Budget Constrained Dynamic Multi-workflow Scheduling in Cloud Computing.- OneMax is not the Easiest Function for Fitness Improvements.- The Cost of Randomness in Evolutionary Algorithms: Crossover Can Save Random Bits.- Multi-objectivization Relaxes Multi-funnel Structures in Singleobjective NK-landscapes.- Decision/Objective Space Trajectory Networks for Multi-objective Combinatorial Optimisation.- On the e_ect of solution representation and neighborhood definition in AutoML Fitness Landscapes.
