Design and development of parameters tuning technique for deep learning model s

Abstract

There are three main ways to handle combinatorial-based problems, one of which is newlineby examining the space of multiple combinations in such a way that either we can newlinefind a solution or it will be proven that the solution is inconsistent. Pruning newlinetechniques and heuristics have not always been able to restrain such problems newlinebecause of either higher computational time or deliberately ignoring some newlinecombinations. As a result, we are not able to find the optimal solution, and using newlinethese techniques, we also won t be able to prove whether the optimality of the newlinecombination that is found is actually optimal or not. In fact, these problems are even newlinemore complex for computer scientists because solving them requires a huge number newlineof combinations. Hence, to solve such challenging and complex combinatorial newlineissues, there are some generic methods known as meta-heuristics. newlineMetaheuristics are a family of various algorithms designed to solve a large and newlinecomplex number of combinatorial problems without even having to adapt them newlinedeeply to each problem. Metaheuristic optimizers have become increasingly popular newlinein recent years because they excel at efficiently optimizing challenging, highdimensional problems that conventional optimization methods struggle with. When newlinedealing with multiple learning models, especially in scenarios involving large newlinedatasets and intricate models, the need for efficient optimization techniques is crucial, newlinemaking these optimization algorithms highly valuable. When it comes to selecting newlinethe most appropriate machine learning techniques for a given task, metaheuristic newlinetechniques like cross-validation and bootstrap aggregation are utilized to compare newlinedifferent model types and pick the most efficient one. Simi newline