Chromosome

GA_CHROMOSOME Function (PV-WAVE Advantage)—Creates a data structure containing unencoded and encoded phenotype information.

Individuals

GA_INDIVIDUAL Function (PV-WAVE Advantage)—Creates a data structure from user supplied phenotypes.

GA_MUTATE Function (PV-WAVE Advantage)—Performs the mutation operation on an individual’s chromosome.

GA_DECODE Function (PV-WAVE Advantage)—Decodes an individual’s chromosome into its binary, nominal, integer and real phenotypes.

GA_ENCODE Function (PV-WAVE Advantage)—Encodes an individual’s binary, nominal, integer and real phenotypes into its chromosome.

Population

GA_POPULATION Function (PV-WAVE Advantage)—Creates a population data structure from user supplied individuals.

GA_RANDOM_POPULATION Function (PV-WAVE Advantage)—Creates a population data structure from randomly generated individuals.

GA_GROW_POPULATION Function (PV-WAVE Advantage)—Adds the individuals in the array individual to an existing population.

GA_MERGE_POPULATION Function (PV-WAVE Advantage)—Creates a new population by merging two populations with identical chromosome structures.

Genetic Algorithm Search and Optimization

GENETIC_ALGORITHM Function (PV-WAVE Advantage)—Optimizes a user-defined fitness function using a tailored genetic algorithm.

NAIVE_BAYES_TRAINER Function (PV-WAVE Advantage)—Trains a Naive Bayes classifier.

NAIVE_BAYES_CLASSIFICATION Function (PV-WAVE Advantage)—Classifies unknown patterns using a previously trained Naive Bayes classifier.

Neural Network Data Structures

MLFF_NETWORK_INIT Function (PV-WAVE Advantage)—Creates a multilayered feedforward neural network.

MLFF_NETWORK Function (PV-WAVE Advantage)—Links and modifies a multilayered feedforward neural network.

MLFF_INITIALIZE_WEIGHTS Function (PV-WAVE Advantage)—Initializes weights for multilayered feedforward neural networks prior to network training using one of four user selected methods.

Forecasting Neural Networks

MLFF_NETWORK_TRAINER Function (PV-WAVE Advantage)—Trains a multilayered feedforward neural network using quasi-Newton backpropagation.

MLFF_NETWORK_FORECAST Function (PV-WAVE Advantage)—Calculates forecasts using trained multilayered feedforward neural networks.

Classification Neural Networks

MLFF_CLASSIFICATION_TRAINER Function (PV-WAVE Advantage)—Trains a multilayered feedforward neural network.

MLFF_PATTERN_CLASSIFICATION Function (PV-WAVE Advantage)—Trains a multilayered feedforward neural network for classification.

Preprocessing Filters

SCALE_FILTER Function (PV-WAVE Advantage)—Scales or unscales continuous data prior to its use in neural network training, testing, or forecasting.

TIME_SERIES_FILTER Function (PV-WAVE Advantage)—Converts time series data to the format required for processing by a neural network.

TIME_SERIES_CLASS_FILTER Function (PV-WAVE Advantage)—Converts time series data sorted within nominal classes in decreasing chronological order to a useful format for processing by a neural network.

UNSUPERVISED_NOMINAL_FILTER Function (PV-WAVE Advantage)—Converts nominal data into a series of binary encoded columns for input to a neural network.

UNSUPERVISED_ORDINAL_FILTER Procedure (PV-WAVE Advantage)—Converts ordinal data into proportions. PV‑WAVE IMSL Statistics Reference