Bayesian Optimization For Parameter Tuning In Complex Ecological Models

Abstract

This study investigates the application of Bayesian Optimization for Parameter Tuning in Complex Ecological Models. The research methodology encompasses data collection, model development, optimization techniques, and performance evaluation. Ecological models are developed based on established ecological principles and theoretical frameworks, aiming to simulate the dynamics of ecological systems. Bayesian Optimization is employed as the primary optimization technique, leveraging probabilistic surrogate models to guide the search for optimal parameter configurations. The performance of the ecological models is evaluated using predefined performance metrics such as accuracy, F1 score, RMSE, and MAE. Results demonstrate the effectiveness of Bayesian Optimization in improving the predictive accuracy and reliability of ecological models. Furthermore, the study evaluates the performance of different optimization techniques and compares their efficacy in parameter tuning. Statistical analysis is conducted to analyze the results and identify significant differences among variables. Overall, this study provides valuable insights into the optimization of ecological models and contributes to the advancement of ecological research and management practices. Through systematic evaluation and optimization, Bayesian Optimization enhances our understanding of complex ecological systems and informs conservation and management strategies.