Sensorimotor Information flow in Genetic Regulatory Network driven control systems
نویسندگان
چکیده
We present results from applying information theory based measures to simulated robots controlled by evolved Genetic Regulatory Networks. Measuring the information flow across sensory and effector surfaces, we create an information profile illustrated using area-proportional Venn diagrams. We examine the relationship between this information profile and various elements of the overall system including the GRN controlling the robot, the environment, the nature of the task for which the GRN controller has been evolved, and evolutionary fitness.
منابع مشابه
Comparison of MLP NN Approach with PCA and ICA for Extraction of Hidden Regulatory Signals in Biological Networks
The biologists now face with the masses of high dimensional datasets generated from various high-throughput technologies, which are outputs of complex inter-connected biological networks at different levels driven by a number of hidden regulatory signals. So far, many computational and statistical methods such as PCA and ICA have been employed for computing low-dimensional or hidden represe...
متن کاملModeling gene regulatory networks: Classical models, optimal perturbation for identification of network
Deep understanding of molecular biology has allowed emergence of new technologies like DNA decryption. On the other hand, advancements of molecular biology have made manipulation of genetic systems simpler than ever; this promises extraordinary progress in biological, medical and biotechnological applications. This is not an unrealistic goal since genes which are regulated by gene regulatory ...
متن کاملAdaptive RBF network control for robot manipulators
TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed netw...
متن کاملPrediction of Driver’s Accelerating Behavior in the Stop and Go Maneuvers Using Genetic Algorithm-Artificial Neural Network Hybrid Intelligence
Research on vehicle longitudinal control with a stop and go system is presently one of the most important topics in the field of intelligent transportation systems. The purpose of stop and go systems is to assist drivers for repeatedly accelerate and stop their vehicles in traffic jams. This system can improve the driving comfort, safety and reduce the danger of collisions and fuel consumption....
متن کاملThe Use of Genetic Algorithms for the Development of Sensorimotor Control Systems
This paper provides a high level review of current and recent work in the use of genetic algorithm based techniques to develop sensorimotor control systems for autonomous agents It focuses on network based controllers and genetic encoding issues associated with them The paper closes with a discussion of the possibility of using arti cial evolutionary techniques to help tackle more speci cally s...
متن کامل