Fusion of Evolutionary Computation and Multidisciplinary Innovations for Emerging Optimization Challenges

Dear Colleagues,

Over decades of development, evolutionary computation (EC) has established itself as a robust population-based optimization paradigm that operates independently of specific problem characteristics. It has demonstrated exceptional performance in tackling challenging problems, such as those in high-dimensional, non-linear, or multi-modal spaces, often outperforming classical mathematical optimization algorithms in terms of generality and practicality. Yet, optimization is an inherently dynamic field where advancements in problem complexity drive corresponding innovations in algorithmic design.

With the advent of the big data era and the rise of artificial intelligence, the characteristics of optimization problems are evolving rapidly. These shifts demand that EC algorithms adopt new, more efficient search strategies to address emerging challenges such as ultra-high dimensionality, excessive optimization costs, and extended computation times.

This Special Issue seeks to explore how techniques from other disciplines can be leveraged to overcome the current challenges in EC while extending its optimization principles to new areas. We welcome contributions that focus on integrating EC with other fields, whether they involve foundational research or practical applications. By fostering such interdisciplinary collaborations, we aim to push the boundaries of both EC and related disciplines, inspiring innovation and vitality across these domains.

Dr. Rui Zhong
Dr. Jun Yu
Guest Editors

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