Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural Computation
Reliable classification using neural networks: a genetic algorithm and backpropagation comparison
Decision Support Systems
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
Future Generation Computer Systems - Special issue: Geocomputation
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
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
Evolving multilayer feedforward neural network using adaptive particle swarm algorithm
International Journal of Hybrid Intelligent Systems
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Ant colony optimization (ACO) algorithm has the powerful ability of searching the global optimal solution, and backpropagation (BP) algorithm has the feature of rapid convergence on the local optima. The proper hybrid of the two algorithms (ACO-BP) may accelerate the evolving speed of neural networks and improve the forecasting precision of the well-trained networks. ACO-BP scheme adopts ACO to search the optimal combination of weights in the solution space, and then uses BP algorithm to obtain the accurate optimal solution quickly. The ACO-BP and BP algorithms were applied to the problems of function approaching and modeling quantitative structure-activity relationships of Herbicides. Experiment results show that the proposed ACO-BP scheme is more efficient and effective than BP algorithm. Furthermore, ACO-BP reliably performs well when the number of hidden nodes varies.