Manuscript Topics

Swarm and Evolutionary Computation (SEC) stands as a vast and burgeoning domain within contemporary computer sciences, encompassing nature-inspired systems capable of exhibiting intelligent behavior. This capability allows them to optimize a wide array of complex real-world scenarios that defy resolution through the direct application of purely theoretical exact approaches.

Historically, the swarm intelligence and evolutionary computation communities operated independently, despite sharing common objectives. Over time, both fields evolved separately. Presently, advancements in these areas have given rise to highly hybrid, interconnected, and self-adaptive frameworks, incorporating ideas from both domains. This necessitates increased collaboration and joint efforts among SEC researchers and practitioners in related fields such as engineering, robotics, and so on.

SEC research proves highly relevant across various real-world domains, ranging from engineering to finance. It finds application in scenarios where optimization is essential for making intelligent decisions or minimizing/maximizing costs/profits.

Notably, SEC systems play a pivotal role in other realms of computer science, such as Machine Learning (ML) and Deep Learning (DL). Hybrid methods leverage SEC algorithms to optimize, train, or design ML and DL systems. Conversely, ML methods enhance the efficiency of non-conforming SEC, addressing issues like premature convergence, lack of selection pressure, or difficulties in maintaining adequate population diversity.

Regardless of the problem's nature—be it single or multi-objective, dynamic or static, continuous or discrete—this Special Issue aims to compile articles showcasing the latest developments within the SEC community. These include successful real-world applications and cutting-edge algorithmic designs. Submissions investigating the algorithmic behavior and dynamics of SEC methods are also encouraged.

Topics of Interest:
• Mathematical or geometric considerations related to search space and fitness landscape.
• Integration of computational intelligence in optimization utilizing SEC principles.
• Machine learning with an evolutionary approach.
• Evolutionary approaches in neural networks.
• Modeling through surrogates.
• Application of operations research in SEC systems.
• Utilizing SEC algorithms for forecasting and data mining.
• Practical applications in the real world such as scheduling or deploying issues, financial engineering, etc.

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