Evolving Neural Networks through a Reverse Encoding Tree
Yang, Chao-Han Huck
Kiani, Narsis A.
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Preprint Posting Date2020-02-03
Online Publication Date2020-09-03
Print Publication Date2020-07
AbstractNeuroEvolution is one of the most competitive evolutionary learning strategies for designing novel neural networks for use in specific tasks, such as logic circuit design and digital gaming. However, the application of benchmark methods such as the NeuroEvolution of Augmenting Topologies (NEAT) remains a challenge, in terms of their computational cost and search time inefficiency. This paper advances a method which incorporates a type of topological edge coding, named Reverse Encoding Tree (RET), for evolving scalable neural networks efficiently. Using RET, two types of approaches – NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search encoding (GS-NEAT) – have been designed to solve problems in benchmark continuous learning environments such as logic gates, Cartpole, and Lunar Lander, and tested against classical NEAT and FS-NEAT as baselines. Additionally, we conduct a robustness test to evaluate the resilience of the proposed NEAT approaches. The results show that the two proposed approaches deliver improved performance, characterized by (1) a higher accumulated reward within a finite number of time steps; (2) using fewer episodes to solve problems in targeted environments, and (3) maintaining adaptive robustness under noisy perturbations, which outperform the baselines in all tested cases. Our analysis also demonstrates that RET expends potential future research directions in dynamic environments. Code is available from https://github.com/HaolingZHANG/ReverseEncodingTree.
CitationZhang, H., Yang, C.-H. H., Zenil, H., Kiani, N. A., Shen, Y., & Tegner, J. N. (2020). Evolving Neural Networks through a Reverse Encoding Tree. 2020 IEEE Congress on Evolutionary Computation (CEC). doi:10.1109/cec48606.2020.9185648
AcknowledgementsThis work was initiated by Living Systems Laboratory at King Abdullah University of Science and Technology (KAUST) lead by Prof. Jesper Tegner and supported by funds from KAUST. Chao-Han Huck Yang was supported by the Visiting Student Research Program (VSRP) from KAUST.
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Conference/Event Name2020 IEEE Congress on Evolutionary Computation (CEC)