A simple genetic algorithm applied to discontinuous regularization

Author

Jensen, John Bach ; Nielsen, Mads

Author_Institution

Dept. of Comput. Sci., Copenhagen Univ., Denmark

fYear

1992

fDate

31 Aug-2 Sep 1992

Firstpage

69

Lastpage

78

Abstract

A simple genetic algorithm without mutation has been applied to discontinuous regularization. The relative slope of the energy-to-fitness function has been introduced as a measure of the rate of convergence. The intuitively better rate of convergence (slow in the beginning, faster in the end) has been shown to be superior to an exponential transformation-function in the present case. A probabilistic model of the performance of the algorithm has been introduced. From this model it has been found that a division into subpopulations decreases the performance, unless more than one computer is available

Keywords

convergence of numerical methods; genetic algorithms; probability; convergence rate; discontinuous regularization; energy-to-fitness function; optimisation; probabilistic model; simple genetic algorithm; Application software; Computational modeling; Computer science; Convergence; Energy measurement; Genetic algorithms; Genetic mutations; Simulated annealing; Stochastic processes; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop

Conference_Location

Helsingoer

Print_ISBN

0-7803-0557-4

Type

conf

DOI

10.1109/NNSP.1992.253706

Filename

253706