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Evolving neuro-controllers for a dynamic system using Structured Genetic Algorithms

delete1998-01-01
delete4
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Dipankar Dasgupta *
DOI：10.1023/A:1008291923124delete
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Abstract

Abstract

En 中文
This paper describes the application of the Structured Genetic Algorithm (sGA) to design neurocontrollers 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 specified vertical angle) and the cart within the limits of the given track.
Keywords:
genetic algorithms
neural networks
pole-cart system
neuro-controller
simulation
gene activation
multi-level chromosome
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Applied Intelligence cover
Applied Intelligence
IF:
3.5
Papers:
7.5K
Citations:
1.7W

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