Non-Weighted Evaluation Function in Multi-Objective Problems


  • Bayadir Abbas Himyari lecturer
  • Azman Yasin
  • Horizon Gitano



Fuzzy Rule extraction, Genetic Algorithms, Non Weighted Evaluation Function, Mutation complementary method


An evaluation function is proposed to deal with multi-objective problems without weight using a new composition method. Improving the evaluation function by reducing its complexity through discarding the weights. The evaluation function is utilized for the optimization of fuzzy rules.

A genetic algorithm is applied as a multi-objective algorithm for fuzzy rules extraction. Simplicity during building fuzzy inference system and reducing the computational complexity is required. The algorithm is applied on AFR data sets.

Author Biography

Bayadir Abbas Himyari, lecturer

computer science


• Dehuri, S., Patnaik, S., Ghosh, A., & Mall, R. (2008). Application of elitist multi-objective genetic algorithm for classification rule generation. Applied soft computing, 8(1), 477-487.

• Fernández, A., López, V., del Jesus, M. J., & Herrera, F. (2015). Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges. Knowledge-Based Systems.

• Gacto, M. J., Alcalá, R., & Herrera, F. (2010). Integration of an index to preserve the semantic interpretability in the multiobjective evolutionary rule selection and tuning of linguistic fuzzy systems. Fuzzy Systems, IEEE Transactions on, 18(3), 515-531.

• Gacto, M. J., Alcalá, R., & Herrera, F. (2011). Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures. Information Sciences, 181(20), 4340-4360.

• Huysmans, J., Baesens, B., & Vanthienen, J. (2006). Using rule extraction to improve the comprehensibility of predictive models. Available at SSRN 961358.

• Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., & Baesens, B. (2011). An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems, 51(1), 141-154.

• Ishibuchi, H., Nakashima, T., & Murata, T. (2001). Three-objective genetics-based machine learning for linguistic rule extraction. Information Sciences, 136(1), 109-133.

• Ishibuchi, H., & Nojima, Y. (2007). Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. International Journal of Approximate Reasoning, 44(1), 4-31.

• Ishibuchi, H., & Yamamoto, T. (2004). Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets and Systems, 141(1), 59-88.

• Mandal, S., & Pal, M. K. K. S. K. (2011). Pattern Recognition and Machine Intelligence.

• Noda, E., Freitas, A., & Lopes, H. S. (1999). Discovering interesting prediction rules with a genetic algorithm. Paper presented at the Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on.

• Noda, E., Freitas, A. A., & Yamakami, A. (2003). A Distributed-Population GA for Discovering Interesting Prediction Rules Advances in Soft Computing (pp. 287-296): Springer.

• Xu, J., & Zhou, X. (2011). Fuzzy-like multiple objective decision making (Vol. 263): Springer.




How to Cite

Himyari, B. A., Yasin, A., & Gitano, H. (2018). Non-Weighted Evaluation Function in Multi-Objective Problems. Asian Journal of Applied Sciences, 6(5).

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