Analysing the Predictivity of Features to Characterise the Search Space

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Durgut R., Aydin M. E., Ihshaish H., Rakib A.

31st International Conference on Artificial Neural Networks, ICANN 2022, Bristol, İngiltere, 6 - 09 Eylül 2022, cilt.13532 LNCS, ss.1-13, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 13532 LNCS
  • Doi Numarası: 10.1007/978-3-031-15937-4_1
  • Basıldığı Şehir: Bristol
  • Basıldığı Ülke: İngiltere
  • Sayfa Sayıları: ss.1-13
  • Anahtar Kelimeler: Feature analysis, Search space characterisation, Supervised machine learning
  • İstanbul Ticaret Üniversitesi Adresli: Hayır

Özet

Exploring search spaces is one of the most unpredictable challenges that has attracted the interest of researchers for decades. One way to handle unpredictability is to characterise the search spaces and take actions accordingly. A well-characterised search space can assist in mapping the problem states to a set of operators for generating new problem states. In this paper, a landscape analysis-based set of features has been analysed using the most renown machine learning approaches to determine the optimal feature set. However, in order to deal with problem complexity and induce commonality for transferring experience across domains, the selection of the most representative features remains crucial. The proposed approach analyses the predictivity of a set of features in order to determine the best categorization.