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20032026
most citedA Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems

407 citations

Showing cs.NEShow all

25 papers · 1 filter

cs.NE2026

Robust Multi-Objective Optimization for Bicycle Rebalancing in Shared Mobility Systems

Diego Daniel Pedroza-Perez, Gabriel Luque, Sergio Nesmachnow +1

Dock-based bike-sharing systems exhibit spatial imbalances between bicycle supply and user demand, often addressed through overnight truck-based rebalancing. This work studies stat…

cs.NE2026

Cooperative Coevolution versus Monolithic Evolutionary Search for Semi-Supervised Tabular Classification

Jamal Toutouh

This paper studies semi-supervised tabular classification in the extreme low-label regime using lightweight base learners. The paper proposes a cooperative coevolutionary method (C…

cs.NE2026

Green Optimization: Energy-aware Design of Metaheuristics by Using Machine Learning Surrogates to Cope with Real Problems

Tomohiro Harada, Enrique Alba, Gabriel Luque

Addressing real-world optimization challenges requires not only advanced metaheuristics but also continuous refinement of their internal mechanisms. This paper explores the integra…

cs.NE2026

Energy-Aware Metaheuristics

Enrique Alba, Tomohiro Harada, Gabriel Luque

This paper presents a principled framework for designing energy-aware metaheuristics that operate under fixed energy budgets. We introduce a unified operator-level model that quant…

cs.NE20251 cited

Multi-population GAN Training: Analyzing Co-Evolutionary Algorithms

Walter P. Casas, Jamal Toutouh

Generative adversarial networks (GANs) are powerful generative models but remain challenging to train due to pathologies suchas mode collapse and instability. Recent research has e…

cs.NE20252 cited

Adversarial attacks to image classification systems using evolutionary algorithms

Sergio Nesmachnow, Jamal Toutouh

Image classification currently faces significant security challenges due to adversarial attacks, which consist of intentional alterations designed to deceive classification models…