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Deja Vu: a hyper heuristic framework with Record and Recall (2R) modules

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Abstract

Despite the success of heuristic methods in solving real-world problems, there are still some difficulties in terms of easily applying them to newly encountered problems, or even new instances of similar problems. In addition, the little or no understanding of why different heuristics work effectively (or not) in certain situations does not facilitate simple choices of which approach to use in which situation. This paper proposes a new hyper heuristic framework named Deja Vu to address these issues. As the names suggests, it retrieves the stored solution of already solved problems for the new but similar problems. This makes the our system efficient and knowledge rich. The performance of Deja Vu is tested on the data sets with varying difficulty. Deja Vu has shown promising results on almost all the occasions.

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Authors and Affiliations

  1. Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan

    Hammad Majeed & Samina Naz

Authors
  1. Hammad Majeed
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  2. Samina Naz
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Correspondence to Hammad Majeed.

Appendix A: Detail of the selected data sets

Appendix A: Detail of the selected data sets

The detail of 70 data sets selected from UCI and WEKA repositories. “Ins#” is the instance count, “Attr#” is the attribute count. Last four datasets are selected from regression domain (Table 10).

Table 10 Data set characteristics
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Majeed, H., Naz, S. Deja Vu: a hyper heuristic framework with Record and Recall (2R) modules. Cluster Comput 22 (Suppl 3), 7165–7179 (2019). https://doi.org/10.1007/s10586-017-1095-x

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