TY - GEN
T1 - Maintaining diversity in EDAs for real-valued optimisation problems
AU - Wallin, David
AU - Ryan, Conor
PY - 2007
Y1 - 2007
N2 - A recent extension applicable to a wide range of discrete EDA algorithms, called Sampling-Mutation, has shown promise on a non-stationary problem, as well as on a hierarchical deceptive problem. In this paper we further the empirical exploration on Ackley, Rosenbrock and Schwefel, three well-known real-valued variable optimisation problems. The EDA on which we perform our experiments is based on learning and simulation of a Bayesian classifier. The population is at each generation divided into classes based on fitness. The benefit that such classes can have on the diversity of the population and also on the performance of the algorithm, will be evaluated and compared to Sampling-Mutation. We will show that Sampling-Mutation can significantly increase the performance of a discrete EDA on said problems by maintaining a higher level of useful population diversity. We also show that an EDA with the use of Sampling-Mutation can be competitive against a generational Genetic Algorithm on this type of problem.
AB - A recent extension applicable to a wide range of discrete EDA algorithms, called Sampling-Mutation, has shown promise on a non-stationary problem, as well as on a hierarchical deceptive problem. In this paper we further the empirical exploration on Ackley, Rosenbrock and Schwefel, three well-known real-valued variable optimisation problems. The EDA on which we perform our experiments is based on learning and simulation of a Bayesian classifier. The population is at each generation divided into classes based on fitness. The benefit that such classes can have on the diversity of the population and also on the performance of the algorithm, will be evaluated and compared to Sampling-Mutation. We will show that Sampling-Mutation can significantly increase the performance of a discrete EDA on said problems by maintaining a higher level of useful population diversity. We also show that an EDA with the use of Sampling-Mutation can be competitive against a generational Genetic Algorithm on this type of problem.
UR - http://www.scopus.com/inward/record.url?scp=49349098236&partnerID=8YFLogxK
U2 - 10.1109/FBIT.2007.132
DO - 10.1109/FBIT.2007.132
M3 - Conference contribution
AN - SCOPUS:49349098236
SN - 0769529992
SN - 9780769529998
T3 - Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007
SP - 795
EP - 800
BT - Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007
T2 - Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007
Y2 - 11 October 2007 through 13 October 2007
ER -