Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Pattern Recognition Letters
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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.