Publications

Interpretable Forecasting of Energy Demand in the Residential Sector

N. Sakkas, S. Yfanti, C. Daskalakis, E. Barbu and M. Domnich

A cooperative coevolutionary hyper-heuristic approach to solve lot-sizing and job shop scheduling problems using genetic programming

Y. Zeiträg, J. R. Figueira, G. Figueira

Learning efficient in-store picking strategies to reduce customer encounters in omnichannel retail

Fábio Neves-Moreira, Pedro Amorim

Drivers of and counterfactuals for the final energy and electricity consumption in EU industry

N. Sakkas, N. Athanasiou

Open data or open access? The case of building data

N. Sakkas, S. Yfanti

Explainable Approaches for Forecasting Building Electricity Consumption

Nikos Sakkas, Sofia Yfanti, Pooja Shah, Nikitas Sakkas, Christina Chaniotakis, Costas Daskalakis, Eduard Barbu and Marharyta Domnich

Technology Readiness Levels (TRLs) in the Era of Co-Creation

Sofia Yfanti, Nikos Sakkas

Building data models and data sharing. Purpose, approaches and a case study on explainable demand response

Nikos Sakkas, Christina Chaniotaki, Nikitas Sakkas

Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

Florian Stelzer, André Röhm, Raul Vicente, Ingo Fischer, Serhiy Yanchuk

Personalized choice model for forecasting demand under pricing scenarios with observational data - The case of attended home delivery

Ö. G. Ali, P. Amorim

Scheduling wagons to unload in bulk cargo ports with uncertain processing times

C. Ferreira, G. Figueira, P. Amorim, A. Pigatti

Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models

Sijben, E. M. C., Tanja Alderliesten, Peter AN Bosman

Memetic Semantic Genetic Programming for Symbolic Regression

Alessandro Leite, Marc Schoenauer

Evolvability degeneration in multi-objective genetic programming for symbolic regression

Liu, Dazhuang, et al.

Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression

Virgolin, Marco, Peter AN Bosman

Deep learning-based auto-segmentation of paraganglioma for growth monitoring

Sijben, E. M. C., et al.

Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals

Sijben, E. M. C., et al.

Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence

M. Domnich, R. Vicente

COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images

D. Shvetsov, J. Ariva, M. Domnich, R. Vicente, D. Fishman

Exploring Commonalities in Explanation Frameworks: A Multi-Domain Survey Analysis

Eduard Barbu, Marharyta Domnich, Raul Vicente, Nikos Sakkas, André Morim

Multi-Objective Genetic Programming for Explainable Reinforcement Learning

Mathurin Videau, Alessandro Ferreira Leite, Olivier Teytaud, Marc Schoenauer