Analysis of algorithm components and parameters: some case studies

Nguyen Dang, Patrick De Causmaecker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Modern high-performing algorithms are usually highly parameterised, and can be configured either manually or by an automatic algorithm configurator. The algorithm performance dataset obtained after the configuration step can be used to gain insights into how different algorithm parameters influence algorithm performance. This can be done by a number of analysis methods that exploit the idea of learning prediction models from an algorithm performance dataset and then using them for the data analysis on the importance of variables. In this paper, we demonstrate the complementary usage of three methods along this line, namely forward selection, fANOVA and ablation analysis with surrogates on three case studies, each of which represents some special situations that the analyses can fall into. By these examples, we illustrate how to interpret analysis results and discuss the advantage of combining different analysis methods.
Original languageEnglish
Title of host publicationLearning and Intelligent Optimization
Subtitle of host publication12th International Conference, LION 12, Kalamata, Greece, June 10–15, 2018, Revised Selected Papers
EditorsRoberto Battiti, Mauro Brunato, Ilias Kotsireas, Panos M. Pardalos
Place of PublicationCham
ISBN (Electronic)9783030053482
ISBN (Print)9783030053475
Publication statusPublished - 2018
EventLearning and Intelligent Optimization Conference (LION 12) - Elite City Resort, Kalamata, Greece
Duration: 10 Jun 201815 Jun 2018
Conference number: 12

Publication series

NameLecture Notes in Computer Science (Theoretical Computer Science and General Issues)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceLearning and Intelligent Optimization Conference (LION 12)
Abbreviated titleLION
Internet address


  • Forward selection
  • fANOVA
  • Ablation analysis with surrogates
  • Parameter analysis


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