Introduction to the theory of neural computation
Introduction to the theory of neural computation
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The nature of statistical learning theory
The nature of statistical learning theory
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Highlights on the Evolutionary Engineering Approach: The EE-Method
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
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Evolutionary Engineering (EE) is defined to be “the art of using evolutionary algorithms approach such as genetic algorithms to build complex systems”. This paper deals with a neural net based system. It analyses ability of genetically trained neural nets to control Simulated robot arm, witch tries to track a moving object. In difference from classical Approaches neural network learning is performed on line, i.e., in real time. Usually systems are built/evolved, i.e., genetically trained separately of their utilization. That is how it is commonly done. It's a fact that evolution process is heavy on time; that's why Real-Time approach is rarely taken into consideration. The results presented in this paper show that such approach (Real-Time EE) is possible. These successful results are essentially due to the “continuity” of the target's trajectory. In EE terms, we express this by the Neighbourhood Hypothesis (NH) concept.