Title :
A note on the population based incremental learning with infinite population size
Author :
Rastegar, R. ; Meybodi, M.R.
Author_Institution :
Comput. Eng. & IT Dept., Amirkabir Univ. of Technol., Tehran
Abstract :
In this paper, we study the dynamical properties of the population based incremental learning (PBIL) algorithm when it uses truncation, proportional, and Boltzmann selection schemas. The results show that if the population size tends to infinity, with any learning rate, the local optima of the function to be optimized are asymptotically stable fixed points of the PBIL
Keywords :
distributed algorithms; genetic algorithms; learning (artificial intelligence); search problems; Boltzmann selection schemas; distribution algorithms; genetic algorithms; infinite population size; population based incremental learning algorithm; Algorithm design and analysis; Biological cells; Buildings; Clustering algorithms; Electronic design automation and methodology; Genetic algorithms; Genetic mutations; H infinity control; Learning automata; Mutual information;
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Conference_Location :
Edinburgh, Scotland
Print_ISBN :
0-7803-9363-5
DOI :
10.1109/CEC.2005.1554685
