Tuning Hopfield Neural Networks with Metaheuristic Hy-perparameter Selection

This chapter explores the critical role of hyperparameter selection in optimizing the performance of continuous Hopfield neural networks (CHN), a type of recurrent neural network introduced by John Hopfield. The energy function, which governs the network's dynamics, is pivotal in finding optimal solutions to combinatorial optimization problems. The chapter delves into the significance of hyperparameters, which are external variables that profoundly impact the network's convergence and performance. Traditional methods for hyperparameter selection, such as trial and error, grid search, and random search, are discussed, along with their limitations in handling complex optimization tasks. The focus then shifts to metaheuristic methods, including genetic algorithms (GA), ant colony optimization (ACO), and particle swarm optimization (PSO), which offer more intelligent and efficient exploration of the hyperparameter search space. The chapter presents a comparative study of these metaheuristic approaches using the Graph Coloring Problem (GCP) as a benchmark. Through rigorous experimentation, the effectiveness and efficiency of GA, ACO, and PSO in selecting optimal hyperparameters for CHN configurations are evaluated. The results provide valuable insights into the strengths and limitations of each method, offering practical guidance for improving the performance and applicability of Hopfield networks in various optimization scenarios. The detailed analysis and experimental results make this chapter a compelling read for those interested in advancing the optimization capabilities of neural networks.