Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Alternatives in automatic function definition: a comparison of performance
Advances in genetic programming
Using prior knowledge and obtaining process insight in data based modelling of bioprocesses
Systems Analysis Modelling Simulation - Special issue on automatic model generation
Digital Control Systems
Genetic Synthesis of Modular Neural Networks
Proceedings of the 5th International Conference on Genetic Algorithms
System Identification using Structured Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Generation of Structured Process Models Using Genetic Programming
Selected Papers from AISB Workshop on Evolutionary Computing
PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System
PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System
Signal path oriented approach for generation of dynamic process models
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Obtaining transparent models of chaotic systems with multi-objective simulated annealing algorithms
Information Sciences: an International Journal
Visualization of neural net evolution
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
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Model construction is usually guided by a trial-erro process, where each iteration is divided into steps: (i) collect or refine the set of equations that direct the system behaviour, normally in differential form, solving them using, most of the time, the S transform, and (ii) fir a set of properties (parameters) in the model obtained using observations taken from the real system.There have been many attempts to automate this process. We will ex tend an approach based on a search of a model of the system in a block diagram representation, where the trial-error process is solved with Genetic Programming. Some modifications over this approach are mode to allow a more general family of models and to enhance its efficiency.