Mechanical engineering design optimization by differential evolution
New ideas in optimization
Journal of Global Optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Prediction of cutting forces in turning process using de-neural networks
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers
Computers and Operations Research
A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems
Computers and Operations Research
A DE-based approach to no-wait flow-shop scheduling
Computers and Industrial Engineering
A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems
Computers and Operations Research
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Differential migration: sensitivity analysis and comparison study
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
International Journal of Bio-Inspired Computation
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
A Lamarckian Hybrid of Differential Evolution and Conjugate Gradients for Neural Network Training
Neural Processing Letters
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
Differential evolution algorithm with ensemble of parameters and mutation strategies
Applied Soft Computing
Design of artificial neural networks using differential evolution algorithm
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Evolving artificial neural networks using adaptive differential evolution
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
RankDE: learning a ranking function for information retrieval using differential evolution
Proceedings of the 13th annual conference on Genetic and evolutionary computation
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Weighting Efficient Accuracy and Minimum Sensitivity for Evolving Multi-Class Classifiers
Neural Processing Letters
Expert Systems with Applications: An International Journal
Enhancing the search ability of differential evolution through orthogonal crossover
Information Sciences: an International Journal
Training neural networks with harmony search algorithms for classification problems
Engineering Applications of Artificial Intelligence
Solving rotated multi-objective optimization problems using differential evolution
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Evolutionary learning using a sensitivity-accuracy approach for classification
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A multi-objective neural network based method for cover crop identification from remote sensed data
Expert Systems with Applications: An International Journal
An informative differential evolution with self adaptive re-clustering technique
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
A differential evolution algorithm for lot-streaming flow shop scheduling problem
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Global hybrid ant bee colony algorithm for training artificial neural networks
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part I
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Differential Evolution for automatic rule extraction from medical databases
Applied Soft Computing
G-HABC Algorithm for Training Artificial Neural Networks
International Journal of Applied Metaheuristic Computing
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Global artificial bee colony algorithm for boolean function classification
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Flexible job shop scheduling using hybrid differential evolution algorithms
Computers and Industrial Engineering
A new back-propagation neural network optimized with cuckoo search algorithm
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
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An evolutionary optimization method over continuous search spaces, differential evolution, has recently been successfully applied to real world and artificial optimization problems and proposed also for neural network training. However, differential evolution has not been comprehensively studied in the context of training neural network weights, i.e., how useful is differential evolution in finding the global optimum for expense of convergence speed. In this study, differential evolution has been analyzed as a candidate global optimization method for feed-forward neural networks. In comparison to gradient based methods, differential evolution seems not to provide any distinct advantage in terms of learning rate or solution quality. Differential evolution can rather be used in validation of reached optima and in the development of regularization terms and non-conventional transfer functions that do not necessarily provide gradient information.