Optinformatics for Evolutionary Learning and Optimization: Methodological Advances and Engineering Applications

This workshop aims to bring together researchers, practitioners, and industry experts to explore recent advances in optinformatics for evolutionary learning and optimization, an emerging paradigm that integrates data-driven knowledge extraction with evolutionary search processes. While evolutionary algorithms have demonstrated strong global optimization capabilities across diverse domains, they traditionally operate in a problem-independent manner, often neglecting valuable knowledge accumulated from previously solved tasks. Optinformatics addresses this limitation by incorporating techniques from data mining, machine learning, and information processing into the optimization pipeline, enabling more efficient, knowledge-aware, and adaptive search.

The workshop will cover methodological innovations in evolutionary learning, including inter-problem knowledge transfer, data-driven feature engineering, and evolutionary reinforcement learning, as well as their applications in real-world scenarios such as vehicle routing, automated machine learning, and complex system optimization. By bridging optimization theory, data-centric learning, and engineering practice, this workshop aims to provide a platform for exchanging novel ideas, fostering interdisciplinary collaboration, and advancing the development of next-generation intelligent optimization frameworks.