Application of multiobjective evolutionary algorithms for dose optimization problems in brachytherapy

Lahanas, M; Milickovic, N; Baltas, D; Zamboglou, N

dc.contributor.author	Lahanas, M	en
dc.contributor.author	Milickovic, N	en
dc.contributor.author	Baltas, D	en
dc.contributor.author	Zamboglou, N	en
dc.date.accessioned	2014-03-01T01:16:09Z
dc.date.available	2014-03-01T01:16:09Z
dc.date.issued	2001	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13953
dc.subject	Aggregation Function	en
dc.subject	Individual Object	en
dc.subject	multiobjective evolutionary algorithm	en
dc.subject	Multiple Objectives	en
dc.subject	Optimal Algorithm	en
dc.subject	Optimal Method	en
dc.subject	Optimization Problem	en
dc.subject	Satisfiability	en
dc.subject	Dose Volume Histogram	en
dc.subject	High Dose Rate	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	GENETIC ALGORITHM	en
dc.subject.other	PROSTATE IMPLANTS	en
dc.subject.other	GENERATION	en
dc.title	Application of multiobjective evolutionary algorithms for dose optimization problems in brachytherapy	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/3-540-44719-9_40	en
heal.identifier.secondary	http://dx.doi.org/10.1007/3-540-44719-9_40	en
heal.language	English	en
heal.publicationDate	2001	en
heal.abstract	In High Dose Rate (HDR) brachytherapy the conventional dose optimization algorithms consider the multiple objectives in form of an aggregate function which combines individual objectives into a single utility value. As a result, the optimization problem becomes single objective, prior to optimization. Up to 300 parameters must be optimized satisfying objectives which are often competing. We use multiobjective dose optimization methods where the objectives are expressed in terms of quantities derived from dose-volume histograms or in terms of statistical parameters of dose distributions from a small number of sampling points. For the last approach we compare the optimization results of evolutionary multiobjective algorithms with deterministic optimization methods. The deterministic algorithms are very efficient and produce the best results. The performance of the multiobjective evolutionary algorithms is improved if a small part of the population is initialized by deterministic algorithms.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PROCEEDINGS	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/3-540-44719-9_40	en
dc.identifier.isi	ISI:000175042500040	en
dc.identifier.volume	1993	en
dc.identifier.spage	574	en
dc.identifier.epage	587	en