Skip to main content

Advertisement

Log in

Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions

  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms, that dynamically adjust selected control parameters or genetic operators during the evolution have been built. Their objective is to offer the most appropriate exploration and exploitation behaviour to avoid the premature convergence problem and improve the final results. One of the adaptive approaches are the adaptive parameter setting techniques based on the use of fuzzy logic controllers, the fuzzy adaptive genetic algorithms (FAGAs). In this paper, we analyse the FAGAs in depth. First, we describe the steps for their design and present an instance, which is studied from an empirical point of view. Then, we propose a taxonomy for FAGAs, attending on the combination of two aspects: the level where the adaptation takes place and the way the Rule-Bases are obtained. Furthermore, FAGAs belonging to different groups of the taxonomy are reviewed. Finally, we identify some open issues, and summarise a few new promising research directions on the topic. From the results provided by the approaches presented in the literature and the experimental results achieved in this paper, an important conclusion is obtained: the use of fuzzy logic controllers to adapt genetic algorithm parameters may really improve the genetic algorithm performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to F. Herrera.

Additional information

This research has been supported by DGICYT PB98-1319.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Herrera, F., Lozano, M. Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions. Soft Computing 7, 545–562 (2003). https://doi.org/10.1007/s00500-002-0238-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-002-0238-y

Keywords

Navigation