A General Evolutionary/Neural Hybrid Approach to Learning Optimization Problems
نویسندگان
چکیده
A method combining the parallel search capabilities of Evolutionary Computation (EC) with the generalization of Neural Networks (NN) for solving learning optimization problems is presented. Assuming a fitness function for potential solutions can be found, EC can be used to explore the solution space, and the survivors of the evolution can be used as a training set for the NN which then generalizes over the entire space. Because the training set is generated by EC using a fitness function, this hybrid approach allows explicit control of training set quality.
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