Selecting genetic algorithms for assembler encoding

• Autorzy: Praczyk T.
• Języki publikacji: EN

Abstrakty

EN

Assembler Encoding makes it possible to use genetic algorithms to construct neural networks. Assembler Encoding represents neural network in a form of the so-called Assembler Encoding Program. The task of the program is to create Network Definition Matrix maintaining all the information necessary to construct the network. In Assembler Encoding different components of Assembler Encoding Programs evolve in separate populations. The evolution in each population can be controlled by a different genetic algorithm. In the experiments reported in the paper the following genetic algorithms were used to control the evolution of programs: Canonical Genetic Algorithm, Steady State Genetic Algorithm and Eugenic Algorithm. The programs created by means of the specified algorithms were used to create neural networks. Then, the networks were tested in the so-called predator-prey problem. The results of the experiments are presented at the end of the paper.

Słowa kluczowe

EN

evolutionary neural networks; genetic algorithms; grasping

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2008
• Tom: Vol. 18, no. 4
• Strony: 477--495
• Opis fizyczny: Bibliogr. 18 poz.

Twórcy

autor

Praczyk T.

• Naval University, Gdynia, Poland,

Bibliografia

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