TY - CONF
T1 - Binary and Gray Enocding in Univariate Marginal Distribution Algorithm, Genetic Algorithm, and Stochastic Hillclimbing
AU - Chakraborty, Uday K.
N1 - This paper employs a Markov model to study the relative performance of binary and Gray coding in the univariate marginal distribution algorithm, genetic algorithm, and stochastic hillclimbing. The results indicate that while there is little difference between the two for all possible functions, Gray coding does not necessarily improve performance for functions which have fewer local optima in the Gray representation than in binary.
PY - 2003/7/16
Y1 - 2003/7/16
N2 - This paper employs a Markov model to study the relative performance of binary and Gray coding in the univariate marginal distribution algorithm, genetic algorithm, and stochastic hillclimbing. The results indicate that while there is little difference between the two for all possible functions, Gray coding does not necessarily improve performance for functions which have fewer local optima in the Gray representation than in binary.
AB - This paper employs a Markov model to study the relative performance of binary and Gray coding in the univariate marginal distribution algorithm, genetic algorithm, and stochastic hillclimbing. The results indicate that while there is little difference between the two for all possible functions, Gray coding does not necessarily improve performance for functions which have fewer local optima in the Gray representation than in binary.
UR - https://www.semanticscholar.org/paper/Binary-and-Gray-Encoding-in-Univariate-Marginal-%2C-%2C-Chakraborty/da1d3556a3cef22181b515dc4347f48fae060934
M3 - Presentation
T2 - Workshop on Analysis and Design of Representations and Operators, International Conference on Genetic and Evolutionary Computation
Y2 - 16 July 2003
ER -