García-Sánchez / Díaz Álvarez / Murphy Applications of Evolutionary Computation

29th European Conference, EvoApplications 2026, Held as Part of EvoStar 2026, Toulouse, France, April 8–10, 2026, Proceedings, Part I
Erscheinungsjahr 2026
ISBN: 978-3-032-23604-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 554 Seiten

Reihe: Lecture Notes in Computer Science

This two-volume set constitutes the refereed proceedings of the 29th European Conference on Applications of Evolutionary Computation, EvoApplications 2026, held as part of EvoStar 2026, in Toulouse, France, during April 8–10, 2026, and co-located with the EvoStar events, EvoCOP, EvoMUSART, and EuroGP.

The 40 full papers and 27 short papers presented in this book were carefully reviewed and selected from 106 submissions. These papers have been organized in the following topical sections:

Applications of Evolutionary Computation, EuroGP & EvoApps Special Joint Track on Evolutionary Machine, Bioinspired Algorithms for Green Computing and Sustainable Complex Systems.

Zielgruppe

Research

Autoren/Hrsg.

García-Sánchez, Pablo

Díaz Álvarez, Josefa

Murphy, Aidan

Inhaltsverzeichnis

.- Applications of Evolutionary Computation
.- CP-MEME: A Hybrid (1+1)-Evolutionary Framework for the Oven Scheduling Problem.
.- Multi-Constrained Evolutionary Molecular Design Framework: An Interpretable Drug Design Method Combining Rule-Based Evolution and Molecular Crossover.
.- EvoTADASHI: Genetic Programming for High-Performance Code Optimization.
.- Feasibility-Preserving Multi-Objective Evolutionary Algorithms with Local Search for the Bi-Objective Maximal Covering Location Problem with Compactness.
.- Unconventional Hexacopters via Evolution and Learning: Performance Gains and New Insights.
.- Evolutionary Design of Specialized Image Compression Operators.
.- Adaptive Curriculum Learning in Genetic Programming–Guided Local Search for Large-Scale Vehicle Routing Problems.
.- A Reinforcement Learning–Inspired Latent Yield-based Adaptive Algorithm Switching Mechanism.
.- Domain-Informed Representation for Evolutionary Sieving in Integral and Module Lattices.
.- Evolutionary Emergence of Distributed Neural Network Controllers in Voxel-Based Soft Robots.
.- Evolving Ternary Patterns and Discriminative Localisation for Basal Cell Carcinoma Detection.
.- On the Impact of Conditional Distribution in Discovered Differential Equation Ensembles.
.- From Cooperation to Hierarchy: A Study of Dynamics of Hierarchy Emergence in a Multi-Agent System.
.- On Counts and Densities of Homogeneous Bent Functions: An Evolutionary Approach.
.- Assessing Evolving and Learning-Based Controllers for Efficient Cursor Control in Human–Computer Interaction.
.- A Quantum-Inspired Genetic Algorithm for Multi-Objective Job-Shop Scheduling.
.- Enhancing Genetic Algorithms with Graph Neural Networks: A Timetabling Case Study.
.- Hybrid Modeling for Predicting the Evolution of Premalignant Cervical Squamous Lesions via Intelligent Agents and Deep Neural Networks.
.- Optimizing Transformers: Metaheuristics for Head Attention Pruning.
.- Evolving Memory-Aware Schedules for Transformer Inference on Systolic Array Accelerators.
.- EuroGP & EvoApps Special Joint Track on Evolutionary Machine
.- Generalisation of Automated Algorithm Selection in Black-Box Optimisation: The Role of Algorithm Portfolio and Learning Model.
.- Quality-Diversity Optimization Meets Neuron-centric Hebbian Learning.
.- Multi-Objective Evolutionary Optimization of Imbalanced Fast Feedforward Networks.
.- Toward Reliable Uncertainty Quantification in Surrogate-Assisted Evolutionary Algorithms via Temporal Conformal Prediction.
.- On Efficient Binarization of Scanned Historical Documents by Training Local Rules of Neural Cellular Automata.
.- PITL-DE: Problem-Independent Transfer Learning in Differential Evolution for Continuous Optimization.
.- Exploring the Impact of Fairness-Aware Criteria in AutoML.
.- Learning to Search: A Reinforcement Learning Agent for Global Optimization.
.- TensorRankNEAT: Fast Preference Learning with Neuroevolution using Tensorization and GPUs.
.- Multi-Objective Evolutionary Neural Architecture Search for Hailo Accelerators.
.- Multi-Objective Optimization for Synthetic-to-Real Style Transfer.
.- Biologically-Inspired Homeostasis for Neuroevolution: Alternating Growth and Pruning Phases.
.- Bioinspired Algorithms for Green Computing and Sustainable Complex Systems
.- Self-Organized Criticality for Green Distributed Computing: A Sandpile-Inspired Model of Energy-Efficient Load Balancing.