The biennial International Conference on Artificial Evolution will celebrate its 17th edition in October 2026 on the French Riviera in Nice (France), a region well known for its famous Promenade des Anglais ("Walkway of the English"), the Festival de Cannes (32 km), the Monaco F1 Grand Prix (20 km), and the world perfume capital Grasse (40 km)...

EA is a non-profit conference dedicated to optimisation techniques that simulate natural evolution. Researchers interested in models of natural evolution and complex systems are encouraged to submit papers to EA. This year we would like to focus on the combination of metaheuristics and reinforcement learning.

Authors of accepted papers are strongly encouraged to attend the conference in person to promote interaction, questions, and discussion among participants. Remote presentations will only be permitted under exceptional circumstances.

All accepted papers will be published in the conference proceedings, which will be available shortly before the conference. A selection of the best oral papers will be published in a volume of the LNCS series of Springer-Verlag (as in previous editions https://link.springer.com/conference/ae).

Researchers involved in Artificial Evolution are interested in the mechanisms of living organisms, such as the mechanism of natural selection of species or the organization of animal societies (ant colonies, bird flights, schools of fish...), and use the latest technical and theoretical tools to extract new knowledge or new problem-solving methods. Mathematics and computer science are obviously well represented in this field, whose scope of application is large: from theoretical or applied problem-solving in combinatorics, game theory, economics, operations research, decision support and machine learning. Many complex industrial problems are currently being tackled and treated using this type of approach, for example, optimizing flood mitigation measures, improving transportation flow or the modeling of gene regulatory networks in bioinformatics...

This year, we would like to focus on combining metaheuristics and reinforcement learning, but contributions on other topics are also welcome.

Theorical Studies as Convergence studies, Fitness Landscape analysis...
Applications of evolutionary paradigms to the real world (life sciences, industry, socio-economic sciences, music, etc.)
Evolutionary robotics, education, visualisation, interactive design and artistic applications
Benchmarks & Comparison Methodologies
Parameter Adaptation and Tuning
Large-scale global optimisation
Memetic algorithms, interactive optimisation, neural networks, etc.
Ensemble Methods & Hyper-heuristics
Distributed evolutionary paradigms
Surrogate model assisted optimisation
Topologies evolution operators
Multi- or Many-modal optimisation
Multi- or Many-objective optimisation
Dynamic & Stochastic optimisation
Hybridisation with other soft computing techniques such as machine learning, deep learning, reinforcement learning or fuzzy logic