New Frontiers in Evolutionary Algorithms: Learning from Nature’s Optimization Strategies

Dear Colleagues,

This Special Issue highlights how nature-inspired principles continue to drive innovation in computational intelligence and optimisation. Evolutionary algorithms and related metaheuristics, modelled on processes such as natural selection, adaptation, and self-organisation, provide powerful frameworks for addressing complex, real-world problems across diverse domains.

The Special Issue welcomes high-quality contributions that present novel theoretical insights, algorithmic developments, and practical implementations in areas such as genetic algorithms, genetic programming, hyper-heuristics, and combinatorial optimisation. Studies that explore bio-inspired mechanisms, interdisciplinary applications, and hybrid approaches integrating machine learning are particularly encouraged, as they demonstrate the creative fusion of natural and artificial problem-solving strategies.

By gathering state-of-the-art research from both academia and industry, this Special Issue aims to advance understanding of evolutionary computation as a nature-inspired paradigm, promoting adaptive, efficient, and robust solutions to the challenges of modern optimisation and design.

Dr. Marko Ðurasević
Guest Editor

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