31 #define _SCL_SECURE_NO_DEPRECATE
54 "Training Proportion",
58 "Percentage of occurrence data to be used to train models.",
61 "Percentage of occurrence data to be used to train models.",
76 "Maximum number of GARP runs to be performed.",
79 "Maximum number of GARP runs to be performed.",
89 "HardOmissionThreshold",
90 "Hard Omission Threshold",
94 "Maximum acceptable omission error. Set to 100% to use only soft omission.",
97 "Maximum acceptable omission error. Set to 100% to use only soft omission.",
107 "ModelsUnderOmissionThreshold",
108 "Models Under Omission Threshold",
112 "Minimum number of models below omission threshold.",
115 "Minimum number of models below omission threshold.",
125 "CommissionThreshold",
126 "Commission Threshold",
130 "Percentage of distribution models to be taken regarding commission error.",
133 "Percentage of distribution models to be taken regarding commission error.",
143 "CommissionSampleSize",
144 "Commission Sample Size",
148 "Number of samples used to calculate commission error.",
151 "Number of samples used to calculate commission error.",
162 "Maximum Number of Threads",
166 "Maximum number of threads of executions to run simultaneously.",
169 "Maximum number of threads of executions to run simultaneously.",
185 "Maximum number of iterations run by the Genetic Algorithm.",
188 "Maximum number of iterations (generations) run by the Genetic Algorithm.",
203 "Defines the convergence value that makes the algorithm stop (before reaching MaxGenerations).",
206 "Defines the convergence value that makes the algorithm stop (before reaching MaxGenerations).",
220 "Maximum number of rules to be kept in solution.",
221 "Maximum number of rules to be kept in solution.",
236 "Number of points sampled (with replacement) used to test rules.",
239 "Number of points sampled (with replacement) used to test rules.",
255 "GARP with best subsets - DesktopGARP implementation",
259 "GARP is a genetic algorithm that creates ecological niche \
260 models for species. The models describe environmental conditions \
261 under which the species should be able to maintain populations. \
262 For input, GARP uses a set of point localities where the species \
263 is known to occur and a set of geographic layers representing \
264 the environmental parameters that might limit the species' \
265 capabilities to survive.",
268 "GARP is a genetic algorithm that creates ecological niche \
269 models for species. The models describe environmental conditions \
270 under which the species should be able to maintain populations. \
271 For input, GARP uses a set of point localities where the species \
272 is known to occur and a set of geographic layers representing \
273 the environmental parameters that might limit the species' \
274 capabilities to survive.",
277 "Anderson, R. P., D. Lew, D. and A. T. Peterson.",
280 "Anderson, R. P., D. Lew, and A. T. Peterson. 2003. \
281 Evaluating predictive models of species' distributions: criteria \
282 for selecting optimal models.Ecological Modelling, v. 162, p. 211 232.",
284 "Ricardo Scachetti Pereira",
285 "rpereira [at] ku.edu",
OM_ALG_DLL_EXPORT AlgMetadata const * algorithmMetadata()
AlgParamMetadata parameters_bs[NUM_PARAM]
std::string _subAlgorithm
OM_ALG_DLL_EXPORT AlgorithmImpl * algorithmFactory()
virtual ~DgGarpBestSubsets()