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Improving a multi-objective evolutionary algorithm to discover quantitative association rules

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Abstract

This work aims at correcting flaws existing in multi-objective evolutionary schemes to discover quantitative association rules, specifically those based on the well-known non-dominated sorting genetic algorithm-II (NSGA-II). In particular, a methodology is proposed to find the most suitable configurations based on the set of objectives to optimize and distance measures to rank the non-dominated solutions. First, several quality measures are analyzed to select the best set of them to be optimized. Furthermore, different strategies are applied to replace the crowding distance used by NSGA-II to sort the solutions for each Pareto-front since such distance is not suitable for handling many-objective problems. The proposed enhancements have been integrated into the multi-objective algorithm called MOQAR. Several experiments have been carried out to assess the algorithm’s performance by using different configuration settings, and the best ones have been compared to other existing algorithms. The results obtained show a remarkable performance of MOQAR in terms of quality measures.

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Acknowledgments

The authors would like to thank Spanish Ministry of Science and Technology, Junta de Andalucia and University Pablo de Olavide for the support under Projects TIN2011-28956-C02, TIN2014-55894-C2-R, P12-TIC-1728 and APPB813097, respectively.

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Authors and Affiliations

  1. Department of Computer Science, University of Seville, ES-41012, Seville, Spain

    M. Martínez-Ballesteros & J. C. Riquelme

  2. Division of Computer Science, Universidad Pablo de Olavide, ES-41013, Seville, Spain

    A. Troncoso & F. Martínez-Álvarez

Authors
  1. M. Martínez-Ballesteros
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  2. A. Troncoso
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  3. F. Martínez-Álvarez
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  4. J. C. Riquelme
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Correspondence to M. Martínez-Ballesteros.

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Martínez-Ballesteros, M., Troncoso, A., Martínez-Álvarez, F. et al. Improving a multi-objective evolutionary algorithm to discover quantitative association rules. Knowl Inf Syst 49, 481–509 (2016). https://doi.org/10.1007/s10115-015-0911-y

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