Designing neural networks using genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Proceedings of the seventh international conference (1990) on Machine learning
Instance-Based Learning Algorithms
Machine Learning
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The nature of statistical learning theory
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Niching methods for genetic algorithms
Niching methods for genetic algorithms
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Genetic Algorithms in Search, Optimization and Machine Learning
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On initial populations of a genetic algorithm for continuous optimization problems
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Replacement strategies to preserve useful diversity in steady-state genetic algorithms
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IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Quasi-random initial population for genetic algorithms
Computers & Mathematics with Applications
Evolutionary product-unit neural networks for classification
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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IEEE Transactions on Information Theory
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
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International Journal of Applied Evolutionary Computation
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This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of generations, selecting the best individuals from each population in the same proportion and combining them to constitute a new initial population. At this point the main loop of an evolutionary algorithm is applied to the new population. The results show that our proposal considerably improves both the efficiency of previous methodologies and also, significantly, their efficacy in most of the data sets. We have carried out our experimentation on twelve data sets from the UCI repository and two complex real-world problems which differ in their number of instances, features and classes.