UNCONSTRAINED OPTIMIZATION IN A STOCHASTIC CELLULAR AUTOMATA SYSTEM
This paper considers a stochastic cellular automata system which models a random dynamical system, and introduces a simple unconstrained optimization problem on such a system to capture hidden characteristics over time. To achieve this goal, we create a random metric which is applied to nearby and faraway locations of automata in order to find hidden characteristics in the automata system over time. Solving the random metric based unconstrained optimization problem, we found that solutions show high and low level fluctuations, depending on the choice of the perturbation parameter \lambda and the corresponding locations. The application of our method to cell concentration data reveals its consistency and adaptability. This work is an expanded version of our previous work .
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Copyright (c) 2010 Journal of Nonlinear Analysis and Optimization: Theory & Applications
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