Evolving Neuro-Controllers for a Dynamic System Using Structured Genetic Algorithms
This paper describes the application of the Structured Genetic Algorithm (sGA) to design neuro-controllers for an unstable physical system. In particular, the approach uses a single unified genetic process to automatically evolve complete neural nets (both architectures and their weights) for controlling a simulated pole-cart system. Experimental results demonstrate the effectiveness of the sGA-evolved neuro-controllers for the task - to keep the pole upright (within a specified vertical angle) and the cart within the limits of the given track.
Dasgupta, D. (1998). Evolving Neuro-Controllers for a Dynamic System Using Structured Genetic Algorithms. Applied Intelligence, 8 (2), 113-121. https://doi.org/10.1023/A:1008291923124