TY - GEN
T1 - On the diversity of diversity
AU - Wallin, David
AU - Ryan, Conor
PY - 2007
Y1 - 2007
N2 - Estimation of Distribution Algorithms (EDA) is an active area of research within the field of Evolutionary Algorithms. While EDAs have shown great promise on difficult problems with strong epistasis between genes, such as hierarchical and deceptive problems, they have not been a choice for non-stationary problems where the target solution changes over time. This work aims to explore the diversity within the population of an EDA using a supervised classifier. We introduce a technique, SamplingMutation, that can help increase the useful diversity within the population. We show that Sampling-Mutation increases the performance of an EDA on a non-stationary problem and a hierarchical problem.
AB - Estimation of Distribution Algorithms (EDA) is an active area of research within the field of Evolutionary Algorithms. While EDAs have shown great promise on difficult problems with strong epistasis between genes, such as hierarchical and deceptive problems, they have not been a choice for non-stationary problems where the target solution changes over time. This work aims to explore the diversity within the population of an EDA using a supervised classifier. We introduce a technique, SamplingMutation, that can help increase the useful diversity within the population. We show that Sampling-Mutation increases the performance of an EDA on a non-stationary problem and a hierarchical problem.
UR - http://www.scopus.com/inward/record.url?scp=79955206153&partnerID=8YFLogxK
U2 - 10.1109/CEC.2007.4424459
DO - 10.1109/CEC.2007.4424459
M3 - Conference contribution
AN - SCOPUS:79955206153
SN - 1424413400
SN - 9781424413409
T3 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
SP - 95
EP - 102
BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
T2 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007
Y2 - 25 September 2007 through 28 September 2007
ER -