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A genetic algorithm for optimization of neural network capable of learning to search for food in a maze

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Abstract

A hypothetical neural scheme is proposed that ensures efficient decision making by an animal searching for food in a maze. Only the general structure of the network is fixed; its quantitative characteristics are found by numerical optimization that simulates the process of natural selection. Selection is aimed at maximization of the expected number of descendants, which is directly related to the energy stored during the reproductive cycle. The main parameters to be optimized are the increments of the interneuronal links and the working-memory constants.

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Authors
  1. E. V. Budilova
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  2. A. T. Terekhin
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  3. S. A. Chepurnov
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Additional information

Biology Department, Moscow State University, Moscow. Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 37, No. 9, pp. 1162–1172, September, 1994.

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Budilova, E.V., Terekhin, A.T. & Chepurnov, S.A. A genetic algorithm for optimization of neural network capable of learning to search for food in a maze. Radiophys Quantum Electron 37, 749–755 (1994). https://doi.org/10.1007/BF01039615

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  • DOI: https://doi.org/10.1007/BF01039615

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