Comparison of Evolutionary and Deterministic Multiobjective Algorithms for Dose Optimization in Brachytherapy
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We compare two multiobjective evolutionary algorithms, with deterministic gradient based optimization methods for the dose optimization problem in high-dose rate (HDR) brachytherapy. The optimization considers up to 300 parameters. The objectives are expressed in terms of statistical parameters, from dose distributions. These parameters are approximated from dose values from a small number of points. For these objectives it is known that the deterministic algorithms converge to the global Pareto front. The evolutionary algorithms produce only local Pareto-optimal fronts. The performance of the multiobjective evolutionary algorithms is improved if a small part of the population is initialized with solutions from deterministic algorithms. An explanation is that only a very small part of the search space is close to the global Pareto front. We estimate the performance of the algorithms in some cases in terms of probability compared to a random optimum search method.
KeywordsEvolutionary Algorithm Planning Target Volume Pareto Front Dose Distribution Objective Space
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