Hopfield neural networks for timetabling: formulations, methods, and comparative results
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
Continuous-State Hopfield Dynamics Based on Implicit Numerical Methods
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Continuous Function Optimisation via Gradient Descent on a Neural Network Approximation Function
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
A portable and scalable algorithm for a class of constrained combinatorial optimization problems
Computers and Operations Research
Genetic Programming and Evolvable Machines
Optimization via Intermittency with a Self-Organizing Neural Network
Neural Computation
Microprocessors & Microsystems
Bifurcations of Renormalization Dynamics in Self-organizing Neural Networks
Neural Information Processing
A Novel Chaotic Neural Network for Function Optimization
Neural Information Processing
Stochastic optimal competitive Hopfield network for partitional clustering
Expert Systems with Applications: An International Journal
Motion planning in order to optimize the length and clearance applying a Hopfield neural network
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Multi-start Stochastic Competitive Hopfield Neural Network for p-Median Problem
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Wireless Personal Communications: An International Journal
A competitive neural network based on dipoles
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
On conditions for intermittent search in self-organizing neural networks
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Fast hopfield neural networks using subspace projections
Neurocomputing
Expert Systems with Applications: An International Journal
Approximate solution of the multiple watchman routes problem with restricted visibility range
IEEE Transactions on Neural Networks
Improved stochastic competitive Hopfield network for polygonal approximation
Expert Systems with Applications: An International Journal
An initiative for a classified bibliography on G-networks
Performance Evaluation
Information Sciences: an International Journal
Parallelism in binary hopfield networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
A saturation binary neural network for crossbar switching problem
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Critical temperatures for intermittent search in self-organizing neural networks
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
A Hopfield neural network applied to the fuzzy maximum cut problem under credibility measure
Information Sciences: an International Journal
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After more than a decade of research, there now exist several neural-network techniques for solving NP-hard combinatorial optimization problems. Hopfield networks and self-organizing maps are the two main categories into which most of the approaches can be divided. Criticism of these approaches includes the tendency of the Hopfield network to produce infeasible solutions, and the lack of generalizability of the self-organizing approaches (being only applicable to Euclidean problems). The paper proposes two new techniques which have overcome these pitfalls: a Hopfield network which enables feasibility of the solutions to be ensured and improved solution quality through escape from local minima, and a self-organizing neural network which generalizes to solve a broad class of combinatorial optimization problems. Two sample practical optimization problems from Australian industry are then used to test the performances of the neural techniques against more traditional heuristic solutions