Self-adaptive parameters in genetic algorithms

Eric Pellerin; Luc Pigeon; Sylvain Delisle

doi:10.1117/12.542156

12 April 2004 Self-adaptive parameters in genetic algorithms

Eric Pellerin, Luc Pigeon, Sylvain Delisle

Proceedings Volume 5433, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI; (2004) https://doi.org/10.1117/12.542156
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

Genetic algorithms are powerful search algorithms that can be applied to a wide range of problems. Generally, parameter setting is accomplished prior to running a Genetic Algorithm (GA) and this setting remains unchanged during execution. The problem of interest to us here is the self-adaptive parameters adjustment of a GA. In this research, we propose an approach in which the control of a genetic algorithm’s parameters can be encoded within the chromosome of each individual. The parameters’ values are entirely dependent on the evolution mechanism and on the problem context. Our preliminary results show that a GA is able to learn and evaluate the quality of self-set parameters according to their degree of contribution to the resolution of the problem. These results are indicative of a promising approach to the development of GAs with self-adaptive parameter settings that do not require the user to pre-adjust parameters at the outset.

Citation Download Citation

Eric Pellerin, Luc Pigeon, and Sylvain Delisle "Self-adaptive parameters in genetic algorithms", Proc. SPIE 5433, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI, (12 April 2004); https://doi.org/10.1117/12.542156

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available