Self-learning optical system based on a genetic-algorithm driven spatial light modulator

Scott D. Carpenter; Peter M. Weber; Janos Peter; Gabor Szabo; Tamas Szakacs; Andras Lorincz

doi:10.1117/12.316567

14 July 1998 Self-learning optical system based on a genetic-algorithm driven spatial light modulator

Scott D. Carpenter, Peter M. Weber, Janos Peter, Gabor Szabo, Tamas Szakacs, Andras Lorincz

Proceedings Volume 3423, Second GR-I International Conference on New Laser Technologies and Applications; (1998) https://doi.org/10.1117/12.316567
Event: Second GR-I International Conference on New Laser Technologies and Applications, 1997, Olympia, Greece

Abstract

We demonstrate the applicability of a genetic algorithm (GA) to control the focus of an adaptive optical system using a liquid crystal spatial light modulator. The optical setup and the algorithm applied are set to fitness type reinforcement for learning. The particular GA developed optimizes the phase shifts in 32 independent pixels, and is biased towards approximating continuous functions that suit the focusing problem. The learning process is demonstrated to work reliably even in the presence of experimental noise.

Citation Download Citation

Scott D. Carpenter, Peter M. Weber, Janos Peter, Gabor Szabo, Tamas Szakacs, and Andras Lorincz "Self-learning optical system based on a genetic-algorithm driven spatial light modulator", Proc. SPIE 3423, Second GR-I International Conference on New Laser Technologies and Applications, (14 July 1998); https://doi.org/10.1117/12.316567

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