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	551	Operation Policies through Dynamic Programming and Genetic Algorithms, for a Reservoir with Irrigation and Water Supply Uses Enthalten in Water resources management Bd. 35, 12.3.2021, Nr. 5, date:3.2021: 1573-1586
	552	Optimal operation of Mula reservoir with combined use of dynamic programming and genetic algorithm Enthalten in Sustainable Water Resources Management Bd. 2, 20.11.2015, Nr. 1, date:3.2016: 1-12
	553	Optimisation of contribution of candidate parents to maximise genetic gain and restricting inbreeding using semidefinite programming (Open Access publication) Enthalten in Genetics, selection, evolution Bd. 39, 11.1.2007, Nr. 1, date:2.2007: 1-23
	554	Optimization of a multiple reservoir system operation using a combination of genetic algorithm and discrete differential dynamic programming: a case study in Mae Klong system, Thailand Enthalten in Paddy and water environment Bd. 3, 12.2.2005, Nr. 1, date:3.2005: 29-38
	555	Optimizationof neural network architecture using genetic programming improvesdetection and modeling of gene-gene interactions in studies of humandiseases Enthalten in BMC bioinformatics Bd. 4, 07.7.2003, Nr. 1, date:12.2003: 1-14
	556	Optimized injection-molding process for thin-walled polypropylene part using genetic programming and interior point solver Enthalten in The international journal of advanced manufacturing technology Bd. 124, 23.11.2022, Nr. 1-2, date:1.2023: 297-313
	557	Optimizing agents with genetic programming: an evaluation of hyper-heuristics in dynamic real-time logistics Enthalten in Genetic programming and evolvable machines Bd. 19, 6.4.2017, Nr. 1-2, date:6.2018: 93-120
	558	Parallel evolution using multi-chromosome cartesian genetic programming Enthalten in Genetic programming and evolvable machines Bd. 10, 20.10.2009, Nr. 4, date:12.2009: 417-445
	559	Pareto Optimal Multigene Genetic Programming for Prediction of Longitudinal Dispersion Coefficient Enthalten in Water resources management Bd. 33, 24.1.2019, Nr. 3, date:2.2019: 905-921
	560	Performance assessment of genetic programming (GP) and minimax probability machine regression (MPMR) for prediction of seismic ultrasonic attenuation Enthalten in Earthquake science Bd. 26, 30.10.2013, Nr. 2, date:4.2013: 147-150

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