Mixed Variable

Mixed-variable optimization (MVO) tackles problems with both continuous and discrete variables, a common scenario in real-world applications like engineering design and machine learning. Current research focuses on developing and improving metaheuristics, such as evolutionary strategies and Bayesian optimization, often hybridizing approaches to handle the complexities of mixed variable spaces, including techniques like target encoding and SHAP value encoding for feature representation. These advancements are crucial for efficiently solving complex optimization problems across diverse fields, leading to improved designs, more effective algorithms, and enhanced decision-making processes.