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Evolving Neuro-Controllers for a Dynamic System Using Structured Genetic Algorithms

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Abstract

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.

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Authors and Affiliations

  1. Department of Mathematical Sciences, The University of Memphis, Memphis, TN, 38152

    Dipankar Dasgupta

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  1. Dipankar Dasgupta
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Dasgupta, D. Evolving Neuro-Controllers for a Dynamic System Using Structured Genetic Algorithms. Applied Intelligence 8, 113–121 (1998). https://doi.org/10.1023/A:1008291923124

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