Differential Evolution Training Algorithm for Feed-Forward Neural Networks
Neural Processing Letters
A Memetic Pareto Evolutionary Approach to Artificial Neural Networks
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
Empirical sensitivity analysis for computational procedures
Proceedings of the 2005 conference on Diversity in computing
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Opposition-Based Learning: A New Scheme for Machine Intelligence
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Performance comparison of self-adaptive and adaptive differential evolution algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Influence of crossover on the behavior of Differential Evolution Algorithms
Applied Soft Computing
Optimization of Neural Networks Weights and Architecture: A Multimodal Methodology
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
Differential Evolution: Fundamentals and Applications in Electrical Engineering
Differential Evolution: Fundamentals and Applications in Electrical Engineering
A 2-Opt based differential evolution for global optimization
Applied Soft Computing
A differential evolution algorithm with self-adapting strategy and control parameters
Computers and Operations Research
A differential evolution based neural network approach to nonlinear system identification
Applied Soft Computing
Engineering Applications of Artificial Intelligence
IEEE Transactions on Evolutionary Computation
Optimization of fed-batch fermentation processes with bio-inspired algorithms
Expert Systems with Applications: An International Journal
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The determination of the optimal neural network topology is an important aspect when using neural models. Due to the lack of consistent rules, this is a difficult problem, which is solved in this paper using an evolutionary algorithm namely Differential Evolution. An improved, simple, and flexible self-adaptive variant of Differential Evolution algorithm is proposed and tested. The algorithm included two initialization strategies (normal distribution and normal distribution combined with the opposition based principle) and a modified mutation principle. Because the methodology contains new elements, a specific name has been assigned, SADE-NN-1. In order to determine the most influential inputs of the models, a sensitivity analysis was applied. The case study considered in this work refer to the oxygen mass transfer coefficient in stirred bioreactors in the presence of n-dodecane as oxygen vector. The oxygen transfer in the fermentation broths has a significant influence on the growth of cultivated microorganism, the accurate modeling of this process being an important problem that has to be solved in order to optimize the aerobic fermentation process. The neural networks predicted the mass transfer coefficients with high accuracy, which indicates that the proposed methodology had a good performance. The same methodology, with a few modifications, and with the best neural network models, was used for determining the optimal conditions for which the mass transfer coefficient is maximized. A short review of the differential evolution methodology is realized in the first part of this article, presenting the main characteristics and variants, with advantages and disadvantages, and fitting in the modifications proposed within the existing directions of research.