Lucas Fenaux
ABSTRACT
PyTorch has profoundly impacted the machine learning (ML) community by allowing researchers of all backgrounds to train models efficiently. While PyTorch is the de facto standard in ML, the evolutionary algorithms (EA) community instead relies on many different libraries, each with low adoption in practice. In an effort to provide a standardized library for EA, packages like LEAP and PyGAD have been developed. However, these libraries fall short in either scalability or usability. In particular, neither of these packages offers efficient support for neuroevolutionary tasks. We argue that the best way to develop a PyTorch-like library for EAs is to build on the already solid foundation of PyTorch itself. We present Gaggle, an efficient PyTorch-based EA library that better supports GPU-based tasks like neuroevolution while maintaining the efficiency of CPU-based problems. We evaluate Gaggle on various problems and find statistically significant improvements in runtime over prior work on problems like training neural networks. In addition to efficiency, Gaggle provides a simple single-line interface making it accessible to beginners and a more customizable research interface with detailed configuration files to better support the EA research community.
- Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016. OpenAI Gym. arXiv:arXiv:1606.01540 Google Scholar
Cross Ref
- John Runwei Cheng and Mitsuo Gen. 2019. Accelerating Genetic Algorithms with GPU Computing: A Selective Overview. Computers & Industrial Engineering 128 (Feb. 2019), 514--525. Google ScholarDigital Library
- Mark A. Coletti, Eric O. Scott, and Jeffrey K. Bassett. 2020. Library for Evolutionary Algorithms in Python (LEAP). In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20). Association for Computing Machinery, New York, NY, USA, 1571--1579. Google ScholarDigital Library
- Li Deng. 2012. The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]. IEEE Signal Processing Magazine 29, 6 (Nov. 2012), 141--142. Google ScholarCross Ref
- Paszke et al. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems, Vol. 32. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.htmlGoogle Scholar
- Ahmed Fawzy Gad. 2021. PyGAD: An Intuitive Genetic Algorithm Python Library. arXiv:arXiv:2106.06158 Google ScholarCross Ref
- Jonatan Kłosko, Mateusz Benecki, Grzegorz Wcisło, Jacek Dajda, and Wojciech Turek. 2022. High Performance Evolutionary Computation with Tensor-Based Acceleration. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '22). Association for Computing Machinery, New York, NY, USA, 805--813. Google ScholarDigital Library
- Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-Based Learning Applied to Document Recognition. Proc. IEEE 86, 11 (Nov. 1998), 2278--2324. Google ScholarCross Ref
- Eric Medvet, Giorgia Nadizar, and Luca Manzoni. 2022. JGEA: A Modular Java Framework for Experimenting with Evolutionary Computation. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '22). Association for Computing Machinery, New York, NY, USA, 2009--2018. Google ScholarDigital Library
Index Terms
- Gaggle: Genetic Algorithms on the GPU using PyTorch
Recommendations
An improved class of real-coded Genetic Algorithms for numerical optimization
Over the last few decades, many improved Evolutionary Algorithms (EAs) have been proposed to tackle different types of optimization problems. Genetic Algorithm (GA) among other canonical algorithms have not shown consistent performance over a range of ...
Self-adjusting the intensity of assortative mating in genetic algorithms
Mate selection plays a crucial role in both natural and artificial systems. While traditional Evolutionary Algorithms (EA) usually engage in random mating strategies, that is, mating chance is independent of genotypic or phenotypic distance between ...
A novel mating approach for genetic algorithms
Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in ...
Comments