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Title: Genetic algorithms for minimal source reconstructions

Under-determined linear inverse problems arise in applications in which signals must be estimated from insufficient data. In these problems the number of potentially active sources is greater than the number of observations. In many situations, it is desirable to find a minimal source solution. This can be accomplished by minimizing a cost function that accounts from both the compatibility of the solution with the observations and for its ``sparseness``. Minimizing functions of this form can be a difficult optimization problem. Genetic algorithms are a relatively new and robust approach to the solution of difficult optimization problems, providing a global framework that is not dependent on local continuity or on explicit starting values. In this paper, the authors describe the use of genetic algorithms to find minimal source solutions, using as an example a simulation inspired by the reconstruction of neural currents in the human brain from magnetoencephalographic (MEG) measurements.
Authors:
;
Publication Date:
OSTI Identifier:
10107088
Report Number(s):
LA-UR--93-4107; CONF-9311135--1
ON: DE94003940; TRN: AHC29401%%27
DOE Contract Number:
W-7405-ENG-36
Resource Type:
Conference
Resource Relation:
Conference: Institute of Electrical and Electronic Engineers (IEEE) asilomar conference on signals, systems, and computers,Pacific Grove, CA (United States),1-3 Nov 1993; Other Information: PBD: [1993]
Research Org:
Los Alamos National Lab., NM (United States)
Sponsoring Org:
USDOE, Washington, DC (United States)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; GENETICS; ALGORITHMS; MAN; BRAIN; SIMULATION; MUTATIONS 550400; GENETICS