Hybrid optimization methods: adaptive control of the evolutionary process using artificial neural networks
Abstract
Hybrid optimization methods: adaptive control of the evolutionary process using artificial neural networks
Incoming article date: 18.01.2025The relevance of the research is determined by the need to solve complex optimization problems under conditions of high dimensionality, noisy data, and dynamically changing environments. Classical methods, such as genetic algorithms, often encounter the problem of premature convergence and fail to effectively adapt to changes in the problem. Therefore, this article focuses on identifying opportunities to enhance the flexibility and efficiency of evolutionary algorithms through integration with artificial neural networks, which allow for dynamically adjusting search parameters during the evolutionary process. The leading approach to addressing this problem is the development of a hybrid system that combines genetic algorithms with neural networks. This approach enables the neural network to adaptively regulate mutation and crossover probabilities based on the analysis of the current state of the population, preventing premature convergence and accelerating the search for the global extremum. The article presents methods for dynamic adjustment of evolutionary parameters using a neural network approach, reveals the principles of the hybrid system's operation, and provides results from testing on the Rastrigin function. The materials of the article hold practical value for further research in the field of optimization, particularly in solving problems with many local minima, where traditional methods may be ineffective. The application of the proposed hybrid model opens new perspectives for developing adaptive algorithms that can be used in various fields of science and engineering, where high accuracy and robustness to environmental changes are required.
Keywords: genetic algorithm, artificial neural network, dynamic tuning, hybrid method, global optimization, adaptive algorithm