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Add the ability to solve any problem using genetic algorithms, should be extensible to allow defining of custom problems and solving heuristics. Properties would include population growth rate, cross over rate, elitists and other genetic metrics.
Use case; An ARIMA type regression algorithm using moving genetic solvers, allowing time series predictors to modulate over time.
The text was updated successfully, but these errors were encountered:
- New infrastructure for genetic algorithms including; selection, mutation and crossover with default implementations.
-- Added random & tounament selection methods
-- Added point crossover method
-- Added random, flip & gaussian mutation methods
- Added new Filters framework along with Roulette Wheel sampling, for sampling objects according to a probability distribution
- Added extension methods to ease common functions
- Added new collection (NSortedList) to overcome SortedList limitations for maintaining sorted collections in ascending and descending order.
Adds: #35 (MVP), Tests Passing.
Add the ability to solve any problem using genetic algorithms, should be extensible to allow defining of custom problems and solving heuristics. Properties would include population growth rate, cross over rate, elitists and other genetic metrics.
Use case; An ARIMA type regression algorithm using moving genetic solvers, allowing time series predictors to modulate over time.
The text was updated successfully, but these errors were encountered: