Chaos-based Modified Morphological Genetic Algorithm for Software Development Cost Estimation

  • Saurabh BilgaiyanEmail author
  • Kunwar Aditya
  • Samaresh Mishra
  • Madhabananda Das
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)


We have proposed a morphological approach based on an evolutionary learning for software development cost estimation (SDCE). The dilation–erosion perceptron (DEP) method which is a hybrid artificial neuron is built on mathematical morphology (MM) framework. This method has its roots in the complete lattice theory. The proposed work also presents an evolutionary learning procedure, i.e., a chaotic modified genetic algorithm (CMGA) to construct the DEP (CMGA) model overcoming the drawbacks arising in the morphological operator’s gradient estimation in the classical learning procedure of DEP. The experimental analysis was conducted on estimation of five different SDCE problems and then analyzed using three performance measurement metrics.


Dilation–Erosion perceptron Evolutionary learning Genetic algorithms Performance measurement metrics and SDCE 


  1. 1.
    Bilgaiyan, S., Mishra, S., Das, M.: A Review of Software Cost Estimation in Agile Software Development using Soft Computing Techniques, 2nd International Conference on Computational Intelligence and Networks, IEEE, pp. 112–117 (2016).Google Scholar
  2. 2.
    Demir, K. A.: 3PR Framework for Software Project Management: People, Process, Product, and Risk. Software Project Management for Distributed Computing, Springer, pp. 143–170 (2017).Google Scholar
  3. 3.
    Singh, J., Sahoo, B.: Software Effort Estimation with Different Artificial Neural Network. 2nd National Conference on Computing, Communication and Sensor Network, Vol. 4, No. 3, pp. 13–17 (2011).Google Scholar
  4. 4.
    Jorgensen, M., Shepperd, M.: A Systematic Review of Software Development Cost Estimation Studies. IEEE Transactions on Software Engineering, Vol. 33, No. 1, pp. 33–53 (2007).Google Scholar
  5. 5.
    The Standish Group. CHAOS Manifesto. Availaible on:, 2013.
  6. 6.
    Arajo, R de A., et al.: An Evolutionary Morphological Approach for Software Development Cost Estimation. Neural Networks, Elsevier, Vol. 32, pp. 285–291 (2012).Google Scholar
  7. 7.
    Gao, W-f, et al.: Particle Swarm Optimization with Chaotic Opposition-based population Initialization and Stochastic Search Technique. Communications in Nonlinear Science and Numerical Simulation, Elsevier, Vol. 17, No. 4, pp. 4316–4327 (2012).Google Scholar
  8. 8.
    Liu, B., Wang, L., et al.: Improved particle Swarm Optimization Combined with Chaos. Chaos, Solitons and Fractals, Vol. 25, No. 1, pp. 1261–1271 (2005).Google Scholar
  9. 9.
    Rahnamayan, S., et al.: Opposition-Based Differential Evolution. IEEE Transactions on Evolutionary Computation, Vol. 12, No. 1, pp. 64–79 (2008).Google Scholar
  10. 10.
    Leung, F. H. F., Lam, H. K., Ling, S. H., Tam, P. K. S.: Tuning of the Structure and Parameters of the Neural Network using an Improved Genetic Algorithm. IEEE Transactions on Neural Networks, Vol. 14, No. 1, pp. 79–88 (2003).Google Scholar
  11. 11.
    Clements, M. P., Hendry, D. F.: On The Limitations of Comparing Mean Square Forecast Errors. Journals of Forecasting, Vol. 12, No. 8, pp. 617–637 (1993).Google Scholar
  12. 12.
    Oliveira, A. L. I., Braga, P. L., Lima, R. M., Cornelio, M. L.: GA-based Method for Feature Selection and Parameters Optimization for Machine Learning Regression Applied to Software Effort Estimation. Information and Software Technology, Elsevier, Vol. 52, No. 1, pp. 6129–6139 (2010).Google Scholar
  13. 13.
    Braga, P. L., et al.: Software Effort Estimation using Machine learning Techniques with Robust Confidence Intervals. IEEE International Conference on Tools with Artificial Intelligence, No. 8, pp. 1595–1600 (2007).Google Scholar
  14. 14.
    Arajo, R. de A., de Oliveira, A. L. I., Soares, S. C. B., Meira, S. R. de L.: Gradient based Morphological Approach for Software Development Cost Estimation. IEEE International Joint Conference on Neural Networks, pp. 588–594 (2011).Google Scholar
  15. 15.
    Arajo, R. de A.: A class of Hybrid Morphological Perceptrons with Application in Time Series Forecasting. Knowledge-Based Systems, Elsevier, Vol. 24, No. 4, pp. 513–529 (2011).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Saurabh Bilgaiyan
    • 1
    Email author
  • Kunwar Aditya
    • 1
  • Samaresh Mishra
    • 1
  • Madhabananda Das
    • 1
  1. 1.School of Computer Engineering, KIIT, Deemed to be UniversityBhubaneswarIndia

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