A dynamic adaptive calibration of the CLONALG immune algorithm

María Cristina Riff, Elizabeth Montero

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

1 Citation (Scopus)

Abstract

The control of parameters during the execution of bio-inspired algorithms is an open research area. In this paper, we propose a new parameter control strategy for the immune algorithm CLONALG. Our approach is based on reinforcement learning ideas. We focus our attention on controlling the number of clones and the number of selected cells which follow a mutation process for improvement. Their values allow a trade-off between intensification and diversification of the search. Our approach provides an efficient and low cost adaptive technique for parameter control. We use instances of the Travelling Salesman Problem that has been tackled before by using CLONALG. The results obtained are very encouraging.

Original languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Adaptive and Intelligent Systems, ICAIS 2009
Pages187-193
Number of pages7
DOIs
Publication statusPublished - 1 Dec 2009
Event2009 International Conference on Adaptive and Intelligent Systems, ICAIS 2009 - Klagenfurt, Austria
Duration: 24 Sep 200926 Sep 2009

Conference

Conference2009 International Conference on Adaptive and Intelligent Systems, ICAIS 2009
Country/TerritoryAustria
CityKlagenfurt
Period24/09/0926/09/09

Keywords

  • Artificial immune algorithms
  • Control
  • Parameter

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Control and Systems Engineering

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