Model-Based Crossover Operator

With the GGA++ tuning algorithm, OPTANO Algorithm Tuner offers an alternative method for GGA to generate offspring. Instead of the classical genetic crossover, a model-based crossover operator can be used. The operator trains a random forest model that predicts the performance of some potential offspring (i.e. a combination of a tournament winner and a non-competitive genome).
For each pair of parents a set of potential offspring is generated (using a targeted sampling method) and the offspring with the best predicted rank (=performance) is selected. Specific details on the algorithm can be found in this paper.

Usage

OPTANO Algorithm Tuner provides several flavors with respect to the internal components that are used by the model-based crossover operator. The default implementation of the OPTANO Algorithm Tuner uses the following set of components:

Learner Model
- StandardRandomForestLearner<ReuseOldTreesStrategy>
Predictor Model
- GenomePredictionForestModel<GenomePredictionTree>
Sampling Strategy
- ReuseOldTreesStrategy

These components, that can be specified via generic type parameters for the AlgorithmTuner, mainly reflect the behavior that is described in the referenced paper. An exception to this is the learner model: The original learner model is implemented in GenomePredictionRandomForest<ReuseOldTreesStrategy and can still be specified. However, internal experiments have shown that a standard random forest often leads to better results in practice.

Another setting which has proven beneficial in some cases is the following configuration:

Additional parameters
- Make sure to use the HammingDistance with --maxRanksCompensatedByDistance approx. 20% of your --miniTournamentSize.

In order to enable the model-based crossover, you need to set the --engineeredProportion to a value larger than 0.