Varying Variants for AncDE with MDV between Target and Trial Vector Measurement


  • Siti Khadijah Mohd Salleh JTMK, Politeknik Ungku Omar, Malaysia
  • Diarmuid O’Donoghue Dept. Computer Science, Maynooth University, Ireland
  • Abd Samad Shibghatullah FTMK, Universiti Teknikal Malaysia Melaka, Malaysia.
  • Zuraida Abal Abas FTMK, Universiti Teknikal Malaysia Melaka, Malaysia.


Different Vector, Ancestor Archive, Ancestor Usage Probability, Ancestor Replacement Probability, Trial Vector, Donor Vector,


This paper compares standard Differential Evolution algorithm with AncDE, which adds a separate cache of recent ancestors that serve as an additional source of highquality genetic information. We compare the solutions produced by both DE and AncDE algorithms using benchmarks of 15 different numeric optimisation problems. Two distinct explorations are presented. The first test is distinct algorithmic variants of AncDE. The second part of this paper defines an MDV attribute and results are presented indicating some interesting differences in MDV between the DE and AncDE algorithms. Our findings indicate that ancestors can help to overcome some of the local variations in solutions quality and improve solution quality by improving population diversity.


T. T. Nguyen, S. Yang, and J. Branke, “Evolutionary dynamic optimization: A survey of the state of the art,” Swarm Evol. Comput., vol. 6, pp. 1–24, 2012.

S. J. Lolle, J. L. Victor, J. M. Young, and R. E. Pruitt, “inheritance of extra-genomic information in Arabidopsis,” vol. 434, no. MARCH, pp. 505–509, 2005.

M. T. Hopkins et al., “De novo genetic variation revealed in somatic sectors of single Arabidopsis plants.,” F1000Research, vol. 2, no. 0, p. 5, 2013.

A. FitzGerald and D. P. O’Donoghue, “Genetic repair for optimization under constraints inspired by Arabidopsis thaliana,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5199 LNCS, pp. 399–408, 2008.

D. Hatton and D. P. O. Donoghue, “Arabidopsis thaliana Inspired Genetic Restoration Strategies,” Int. J. Biometrics Bioinforma., vol. 7, no. 1, pp. 35–48, 2013.

R. Storn and K. Price, “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,” J. Glob. Optim., pp. 341–359, 1997.

M. A. Babu et al., “Application of Self Adaptive Differential Evolution Algorithm for Optimal Placement and Sizing of Renewable Dg Sources in Distribution Network Including Different Load.”

R. Sawant, D. Hatton, and D. P. O’Donoghue, “An ancestor based extension to Differential Evolution (AncDE) for Single-Objective Computationally Expensive Numerical Optimization,” in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, 2015, pp. 3228–3234.

G. Mendel, “Experiments in Plant Hybridization,” J. R. Hortic. Soc., vol. IV, no. 1865, pp. 3–47, 1865.

S. Das and P. Suganthan, “Differential evolution: A survey of the stateof-the-art,” IEEE Trans. Evol. Comput., vol. 15, no. 1, pp. 4–31, 2011.

T. Guide and T. S, Natural Computing Series. 2003.

A. K. Qin and P. N. Suganthan, “Self-adaptive differential evolution algorithm for numerical optimization,” IEEE Congr. Evol. Comput., pp. 1785–1791, 2005.

J.-P. Watson, “An Introduction to Fitness Landscape Analysis and Cost Models for Local Search,” Handb. Metaheuristics, vol. 146, no. International Series in Operations Research & Management Science, pp. 599–623, 2010.

J. J. Liang, B. Y. Qu, P. N. Suganthan, and Q. Chen, “Problem Definitions and Evaluation Criteria for the CEC 2015 Competition on Learning-based Real-Parameter Single Objective Optimization,” Tech. Report201411A, Comput. Intell. Lab. Zhengzhou Univ. Zhengzhou China Tech. Report, Nanyang Technol. Univ. Singapore, no. November 2014, 2014.




How to Cite

Mohd Salleh, S. K., O’Donoghue, D., Shibghatullah, A. S., & Abal Abas, Z. (2018). Varying Variants for AncDE with MDV between Target and Trial Vector Measurement. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-5), 155–160. Retrieved from