Soft Computing

, Volume 18, Issue 8, pp 1603–1614 | Cite as

Hybrid back-propagation training with evolutionary strategies

  • José Parra
  • Leonardo Trujillo
  • Patricia Melin
Methodologies and Application


This work presents a hybrid algorithm for neural network training that combines the back-propagation (BP) method with an evolutionary algorithm. In the proposed approach, BP updates the network connection weights, and a (\(1+1\)) Evolutionary Strategy (ES) adaptively modifies the main learning parameters. The algorithm can incorporate different BP variants, such as gradient descent with adaptive learning rate (GDA), in which case the learning rate is dynamically adjusted by the stochastic (\(1+1\))-ES as well as the deterministic adaptive rules of GDA; a combined optimization strategy known as memetic search. The proposal is tested on three different domains, time series prediction, classification and biometric recognition, using several problem instances. Experimental results show that the hybrid algorithm can substantially improve upon the standard BP methods. In conclusion, the proposed approach provides a simple extension to basic BP training that improves performance and lessens the need for parameter tuning in real-world problems.


Neural networks Back-propagation  Evolutionary strategies 



First author was supported by scholarship 263888 from Consejo Nacional de Ciencia y Tecnología (CONACYT) of México. Corresponding author also thanks the Departamento de Ingeniería Eléctrica y Electrónica at the Instituto Tecnológico de Tijuana. Additionally, partial funding for this work was given by CONACYT (Mexico) Basic Science Research Grant No. 178323.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • José Parra
    • 1
  • Leonardo Trujillo
    • 1
  • Patricia Melin
    • 1
  1. 1.Instituto Tecnológico de TijuanaTijuanaMéxico

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