Genetic algorithms for maximum likelihood parameter estimation

13 January 2003

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

p. 2716-2719
https://doi.org/10.1109/icassp.1989.267029

Abstract

The authors introduce genetic algorithms (GAs) to solve the exact maximum-likelihood equations arising in a typical signal processing problem. GAs are an efficient and highly parallel way of maximizing complex, multimodal, multivariable functions. They are based on concepts borrowed from natural genetic evolution, involving reproduction, crossover, and mutation of a continuously evolving population of parameter estimates. An outline is presented of a GA for maximizing a Gaussian likelihood function, and it is shown that it can outperform existing high-performance parameter estimation algorithms under difficult conditions Author(s) Sharman, K.C. Dept. of Electron. & Electr. Eng., Strathclyde Univ., Glasgow, UK McClurkin, G.D.

Keywords

This publication has 1 reference indexed in Scilit:

Exact maximum likelihood estimation of superimposed exponential signals in noise
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2005