User-Centric Algorithms and Fair Applications using AI and Evolutionary Computation
Maastricht, Netherlands, 21-26 June 2026
Organized by João Soares (jan@isep.ipp.pt), Fernando Lezama, Jose Almeida, Ansel Rodriguez, Zita Vale
Modern energy and mobility systems are increasingly data-rich, distributed, and user- interactive. Decisions about charging, routing, pricing, and demand response now directly affect people and communities. This session spotlights user-centric and fairness-aware methods that combine AI and evolutionary computation (EC) to deliver reliable, transparent, and equitable outcomes across smart energy and smart mobility ecosystems, extending the observability and analytics emphasis of the original template to human-impacting use cases. As infrastructures scales (renewables, EVs, DERs, micromobility), optimization must balance multiple criteria such as accuracy, cost, resilience, privacy, and fairness under uncertainty and dynamic environments. EC and other computational-intelligence paradigms naturally explore multi-objective trade-offs, reveal Pareto frontiers, and yield interpretable choices for operators, policymakers, and end users.
Scope and Topics
Preferred topics are within Energy and Smart Mobility domains (but not restricted), using computational-intelligence techniques applied to:
- Evolutionary multi-objective design: fairness–accuracy–cost–robustness trade-offs; constraint handling (network limits, service levels); many-objective EC.
- Observability & data pipelines: grid and fleet observability; feature learning for demand, charging, routing; anomaly detection (power quality, energy theft, unsafe driving/charging).
- Smart mobility applications: EV charging coordination, V2G/V2X, equitable charger siting, multimodal routing, demand-responsive transit, micromobility balancing.
- Smart energy applications: inclusive demand flexibility, prosumer scheduling, DER coordination, distribution-grid monitoring and stability, forecasting of load/RES/traffic-energy couplings.
- Search & AutoML for fairness: NAS/HPO with fairness constraints; data-centric debiasing; synthetic data for rare-user segments.
- Causal & counterfactual methods: counterfactual fairness, causal discovery for spurious-correlation mitigation; actionable recourse for users.
- Trust, privacy, and robustness: DP + robustness + fairness tri-objective setups; calibration/uncertainty for safety-critical decisions.
- Human-in-the-loop and governance: interpretable policies, stakeholder-aware KPIs, alignment with standards/regulation.
- Benchmarking: reproducible datasets/workflows for combined energy-mobility tasks; realistic network/traffic constraints and power-flow validation.
Important Dates
Paper submission due: 31 January 2026
Notification of acceptance: 15 March 2026
Final paper submission and early registration: 15 April, 2026
How to submit a paper
Further related bibliography
- [1] Almeida, J., Soares, J., Lezama, F., Vale, Z., & Francois, B. (2023). Comparison of evolutionary algorithms for solving risk-based energy resource management considering conditional value-at-risk analysis. Mathematics and Computers in Simulation, 2024.
- [2] Rodríguez-González, A. Y., Lezama, F., Martínez-López, Y., Madera, J., Soares, J., & Vale, Z. (2022). WCCI/GECCO 2020 Competition on Evolutionary Computation in the Energy Domain: An overview from the winner perspective. Applied Soft Computing, 125, 109162.
- [3] Lezama, F., Soares, J., Vale, Z., Rueda, J., Rivera, S., & Elrich, I. (2019). 2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results. Swarm and evolutionary computation, 44, 420-427.
- [4] Lezama, F., Soares, J., Faia, R., & Vale, Z. (2019, July). Hybrid-adaptive differential evolution with decay function (HyDE-DF) applied to the 100-digit challenge competition on single objective numerical optimization. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 7-8).
Organizers
