By Topic

Co-evolutionary learning on noisy tasks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Darwen, P.J. ; Dept. of Comput. Sci. & Electr. Eng., Queensland Univ., Brisbane, Qld., Australia ; Pollack, J.B.

The paper studies the effect of noise on co-evolutionary learning, using Backgammon as a typical noisy task. It might seem that co-evolutionary learning would be ill-suited to noisy tasks: genetic drift causes convergence to a population of similar individuals, and on noisy tasks it would seem to require many samples (i.e., many evaluations and long computation time) to discern small differences between similar population members. Surprisingly, the paper learns otherwise: for small population sizes, the number of evaluations does have an effect on learning; but for sufficiently large populations, more evaluations do not improve learning at all-population size is the dominant variable. This is because a large population maintains more diversity, so that the larger differences in ability can be discerned with a modest number of evaluations. This counter-intuitive result means that co-evolutionary learning is a feasible method for noisy tasks, such as military situations and investment management

Published in:

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on  (Volume:3 )

Date of Conference: