  NeuroEvolution is one of the most competitive evolutionary learning
frameworks 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 algorithms. The results show that the two proposed strategies
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.
