Analysing the Predictivity of Features to Characterise the Search Space

Rafet Durgut, Mehmet Emin Aydin, Hisham Ihshaish, Rakib Abdur

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning - ICANN 2022
EditorsElias Pimenidis, Mehmet Aydin, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas
Number of pages13
ISBN (Electronic)978-3-031-15937-4
ISBN (Print)978-3-031-15936-7
Publication statusE-pub ahead of print - 7 Sept 2022
Event31st International Conference on Artificial Neural Networks - The University of the West of England (UWE Bristol) , Bristol, United Kingdom
Duration: 6 Sept 20229 Sept 2022

Publication series

NameLecture Notes in Computer Sciene
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference31st International Conference on Artificial Neural Networks
Abbreviated titleICANN22
Country/TerritoryUnited Kingdom
Internet address


  • feature analysis
  • search space characterisation
  • supervised machine learning


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