TL;DR
An improved multi-operator differential evolution algorithm, called NN-IMODE, tailored for comprehensive Feedforward Neural Networks optimization, encompassing the number of hidden layers, neurons per layer, weights, biases, and activation functions, is introduced.
Accurate nonlinear modeling underpins every layer of modern engineering. Sampling neural network (SNN) is a new fitting network, which has a simple structure, clear physical concept, concise algorithm, stable performance, and adopts a new error diffusion training method similar to the diffusion of neural stimuli in liv...
A systematic comparison of backpropagation and ten variants of a genetic algorithm for training multi-layer perceptrons (MLPs), with particular focus on the role of crossover operators, helps clarify when gradient-free training is competitive and which evolutionary operators drive its effectiveness.
Deep neural networks are commonly trained using adaptive optimization methods such as Adam because they converge quickly and perform well under stochastic training conditions. Despite these advantages, recent research has revealed several important drawbacks of Adam. In particular, the optimizer tends to converge towar...
Results indicate that coupling high-order Runge-Kutta numerical integration with population-based stochastic search offers an effective, gradient-free strategy for MLP training and other nonlinear optimization problems.
Four evolutionary optimization algorithms and six activation functions are investigated, resulting in 24 different configurations, and their performance on the Inverted Double Pendulum environment is evaluated, where one algorithm consistently exhibits stable learning across most activation functions.
It is demonstrated that hyperparameter optimization dynamics depend heavily on dataset complexity, where computational efficiency is the primary differentiator for simpler classification tasks, but optimization architecture selection becomes critical for navigating challenging medical imaging applications.
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