Ensemble Genetic Programming
This work addresses the need for more effective genetic programming methods in machine learning, though it appears incremental as it builds on existing ensemble learning paradigms.
The authors tackled the problem of improving genetic programming for binary classification by developing Ensemble GP, which achieved significantly better results than standard GP with smaller models on eight problems and succeeded on a hard problem where other methods failed.
Ensemble learning is a powerful paradigm that has been usedin the top state-of-the-art machine learning methods like Random Forestsand XGBoost. Inspired by the success of such methods, we have devel-oped a new Genetic Programming method called Ensemble GP. The evo-lutionary cycle of Ensemble GP follows the same steps as other GeneticProgramming systems, but with differences in the population structure,fitness evaluation and genetic operators. We have tested this method oneight binary classification problems, achieving results significantly betterthan standard GP, with much smaller models. Although other methodslike M3GP and XGBoost were the best overall, Ensemble GP was able toachieve exceptionally good generalization results on a particularly hardproblem where none of the other methods was able to succeed.