Global optimization for artificial neural networks: A tabu search application
References (49)
- et al.
Tabu search and design optimisation
Computer Aided Design
(1991) A derivative-free exploratory tool for function minimisation based on tabu search
Advances in Engineering Software
(1994)On the approximate realization of continuous mappings by neural networks
Neural Networks
(1989)Future paths for integer programming and links to artificial intelligence
Computer Operations Research
(1986)- et al.
Global optimization of statistical functions with simulated annealing
Journal of Econometrics
(1994) - et al.
Multilayer feedforward networks are universal approximators
Neural Networks
(1989) - et al.
Tabu search for the multilevel generalized assignment problem
European Journal of Operational Research
(1995) - et al.
Back propagation learning with trinary quantization of weight updates
Neural Networks
(1991) - et al.
Creating artificial neural networks that generalize
Neural Networks
(1991) - et al.
Improving the convergence of the back propagation algorithm
Neural Networks
(1992)
Back propagation, weight-elimination and time series prediction
Application of the back propagation neural network algorithm with monotonicity constraints for two-group classification problems
Decision Sciences
(1993)
Nonlinear optimization of constrained functions using tabu search
International Journal of Mathematical Education in Science and Technology
(1993)
Stepsize variation methods for accelerating the back propagation algorithm
A tabu search procedure for multicommodity location/allocation with balancing requirements
(1991)
Tabu search techniques: A tutorial and an application to neural networks
Operations Research Spektrum
(1989)
Speech recognition with back propagation
On learning the derivatives of an unknown mapping with multilayer feedforward networks
Tabu search
Tabu search: A tutorial
Interfaces
(1990)
Bandwidth packing: A tabu search approach
Management Science
(1993)
Mathematical Theory of Economic Behavior
Algorithms for the maximum satisfiability problem
The tabu search metaheuristic: How we used it
Annals of Mathematics and Artificial Intelligence
(1990)
Cited by (130)
Machine learning algorithms to predict flow boiling pressure drop in mini/micro-channels based on universal consolidated data
2021, International Journal of Heat and Mass TransferA hybrid computational intelligence approach to predict spectral acceleration
2019, Measurement: Journal of the International Measurement ConfederationCitation Excerpt :The main drawback of employing ANNs in prediction problems is the possibility of being trapped in local minima because of inefficient training [23]. Optimization algorithms are proposed as effective tools for training ANNs to overcome this issue [24–30]. Genetic algorithm (GA) as one of the most applicable evolutionary-based algorithms has been tackled for training ANNs several times e.g., da Silva Ferreira [31] for evaluating power coupling efficiency of photonic couplers, Yuce et al. [32] for energy management in the domestic sector, Azadeh et al. [33] for optimizing machinery productivity.
New machine learning-based prediction models for fracture energy of asphalt mixtures
2019, Measurement: Journal of the International Measurement ConfederationApplication of metaheuristic algorithms to the identification of nonlinear magneto-viscoelastic constitutive parameters
2018, Journal of Magnetism and Magnetic MaterialsNext generation prediction model for daily solar radiation on horizontal surface using a hybrid neural network and simulated annealing method
2017, Energy Conversion and Management
- 1
Supported in part by the Mississippi Alabama Sea Grant Consortium through NOAA.
Copyright © 1998 Published by Elsevier B.V.