ABSTRACT
Denoising autoencoder genetic programming (DAE-GP) is an estimation of distribution genetic programming (EDA-GP) algorithm. It uses denoising autoencoder long short-term memory networks as probabilistic model to replace the standard mutation and recombination operators of genetic programming (GP). Recent work has shown several advantages regarding solution length and overall performance of DAE-GP when compared to GP. However, training a neural network at each generation is computationally expensive, where model training is the most time consuming process of DAE-GP. In this work, we propose pretraining to reduce the runtime of the DAE-GP. In pretraining, the neural network is trained preceding the evolutionary search. In experiments on 8 real-world symbolic regression tasks we find that DAE-GP with pretraining has a reduced overall runtime of an order of magnitude while generating individuals with similar or better fitness.
- François Chollet. 2015. keras. https://github.com/fchollet/keras.Google Scholar
- Félix Antoine Fortin, François Michel De Rainville, Marc André Gardner, Marc Parizeau, and Christian Gagńe. 2012. DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research 13, 1 (2012), 2171--2175.Google ScholarDigital Library
- Christian Olmscheid, David Wittenberg, Dominik Sobania, and Franz Rothlauf. 2021. Improving Estimation of Distribution Genetic Programming with Novelty Initialization. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (Lille, France) (GECCO '21). Association for Computing Machinery, New York, NY, USA, 261--262.Google ScholarDigital Library
- Dirk Schweim, David Wittenberg, and Franz Rothlauf. 2021. On Sampling Error in Evolutionary Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (Lille, France) (GECCO '21). Association for Computing Machinery, New York, NY, USA, 43--44.Google ScholarDigital Library
- Dirk Schweim, David Wittenberg, and Franz Rothlauf. 2021. On sampling error in genetic programming. Natural Computing 21, 2 (2021), 1--14.Google Scholar
- Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning (ICML'08). ACM, Helsinki, Finland, 1096--1103. Google ScholarDigital Library
- David Wittenberg. 2022. Using Denoising Autoencoder Genetic Programming to control Exploration and Exploitation in Search. In Proceedings of the 25th European Conference on Genetic Programming (EuroGP'22). Springer, Madrid, Spain, 96--111.Google ScholarDigital Library
- David Wittenberg and Franz Rothlauf. 2022. Denoising Autoencoder Genetic Programming for Real-World Symbolic Regression. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (Boston, Massachusetts) (GECCO '22). Association for Computing Machinery, New York, NY, USA, 612--614.Google ScholarDigital Library
- David Wittenberg and Franz Rothlauf. 2023. Small Solutions for Real-World Symbolic Regression Using Denoising Autoencoder Genetic Programming. In Genetic Programming: 26th European Conference, EuroGP 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12--14, 2023, Proceedings. Springer, 101--116.Google ScholarDigital Library
- David Wittenberg, Franz Rothlauf, and Dirk Schweim. 2020. DAE-GP: Denoising Autoencoder LSTM Networks as Probabilistic Models in Estimation of Distribution Genetic Programming. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (Cancún, Mexico) (GECCO '20). ACM, New York, NY, USA, 1037--1045.Google ScholarDigital Library
Index Terms
- Pretraining Reduces Runtime in Denoising Autoencoder Genetic Programming by an Order of Magnitude
Recommendations
Denoising autoencoder genetic programming for real-world symbolic regression
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionDenoising Autoencoder Genetic Programming (DAE-GP) is a novel neural-network based estimation of distribution genetic programming algorithm that uses denoising autoencoder long short-term memory networks as probabilistic model to replace the standard ...
Small Solutions for Real-World Symbolic Regression Using Denoising Autoencoder Genetic Programming
Genetic ProgrammingAbstractDenoising Autoencoder Genetic Programming (DAE-GP) is a model-based evolutionary algorithm that uses denoising autoencoder long short-term memory networks as probabilistic model to replace the standard recombination and mutation operators of ...
Segment-based genetic programming
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computationGenetic Programming (GP) is one of the successful evolutionary computation techniques applied to solve classification problems, by searching for the best classification model applying the fitness evaluation. The fitness evaluation process greatly ...
Comments