UNCONSTRAINED OPTIMIZATION IN A STOCHASTIC CELLULAR AUTOMATA SYSTEM

This paper considers a stochastic cellular automata system which models arandom dynamical system, and introduces a simple unconstrained optimization problemon such a system to capture hidden characteristics over time. To achieve this goal, wecreate a random metric which is applied to nearby and faraway locations of automata inorder to find hidden characteristics in the automata system over time. Solving the randommetric based unconstrained optimization problem, we found that solutions show high andlow level fluctuations, depending on the choice of the perturbation parameter \lambda and thecorresponding locations. The application of our method to cell concentration data revealsits consistency and adaptability. This work is an expanded version of our previous work [5].