The increasing complexity of machine learning models has led to a critical need for effective parameter optimization techniques to achieve optimal performance. This paper explores the application of evolutionary computation techniques in parameter optimization for machine learning models. Evolutionary algorithms, such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO), have been shown to be potent tools for addressing this challenge. The integration of these techniques with machine learning models allows for the exploration of a vast solution space while maintaining robustness and efficiency. This paper provides an overview of the methodologies used in evolutionary computation for parameter optimization, discusses their strengths and limitations, and illustrates their efficacy through several case studies. Furthermore, it examines the potential advancements in the field and the role of hybridization with other optimization methods. The findings highlight the importance of evolutionary computation techniques in enhancing the performance and adaptability of machine learning models.
White, E. Evolutionary Computation Techniques for Parameter Optimization in Machine Learning Models. Transactions on Applied Soft Computing, 2021, 3, 22. https://doi.org/10.69610/j.tasc.20210921
AMA Style
White E. Evolutionary Computation Techniques for Parameter Optimization in Machine Learning Models. Transactions on Applied Soft Computing; 2021, 3(2):22. https://doi.org/10.69610/j.tasc.20210921
Chicago/Turabian Style
White, Emma 2021. "Evolutionary Computation Techniques for Parameter Optimization in Machine Learning Models" Transactions on Applied Soft Computing 3, no.2:22. https://doi.org/10.69610/j.tasc.20210921
APA style
White, E. (2021). Evolutionary Computation Techniques for Parameter Optimization in Machine Learning Models. Transactions on Applied Soft Computing, 3(2), 22. https://doi.org/10.69610/j.tasc.20210921
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