Evolutionary Strategies for Parameter Optimization in Deep Learning Models: A Meta-Heuristic Approach to Hyperparameter Tuning
Keywords:
Evolutionary Strategies, Hyperparameter Optimization, Deep Learning, Genetic Algorithms, Meta-Heuristics, Neural Architecture Search, Automated Machine Learning (AutoML), Model Tuning.Abstract
The effectiveness of Deep Learning (DL) models is fundamentally determined by the set of their hyperparameters, which include the settings, which control the learning process and the structure, i.e. the learning rate, network depth, dropout rate, and the choice of the optimizer. Hyperparameter optimization (HPO) may use manual and grid search techniques, which are often computationally infeasible and incapable of solving complex and high-dimensional search spaces. In this paper, Evolutionary Strategies (ES) is reviewed and analyzed as a powerful, population-based meta-heuristic method of automated HPO in DL. Based on biological evolution, ES uses selection, crossover and mutation mechanisms to repeatedly modify a population of candidate hyperparameter combinations to areas of the performance landscape that are optimal. The underlying methodology that we synthesize includes the knowledge of other adjacent areas such as Bayesian optimization in tuning of SVM, multimodal learning problems, or any other area where efficient model tuning is needed. The article has presented the most important types of ES variants in a systematized discussion (e.g., Genetic Algorithms, Covariance Matrix Adaptation Evolution Strategy), how they can be combined with DL training pipelines and have missed the comparison of the performance of these two categories of HPO with other types of HPO such as Random Search, Bayesian Optimization, and Gradient-Based Optimization. Empirical studies in a wide variety of fields (including image registration (where CNNs are used) and time-series prediction (where LSTMs are used) and radiomics-based medical disease diagnostics show that ES is always able to determine high-performing models, and in many cases, it has a better exploration behavior in rugged, non-convex search spaces. Nonetheless, they have issues, such as, the cost per evaluation is too high, they have to be parallelized, and developed efficient fitness functions and genetic representation of neural architectures. The paper ends by giving a list of future research directions on the border of evolutionary computation and DL, including neuroevolution to optimize joint architecture and parameter search and multi-objective optimization to find balance between accuracy and efficiency, and the generalization of such concepts to apply to emergent models in finance, IoT systems, and multimodal AI.
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