Acceleration of cellular automata algorithms using genetic algorithms
The following problem is solved: Given a Cellular Automaton with continuous state space which simulates a physical system or process, use a Genetic Algorithm in order to find a Cellular Automaton with discrete state space, having the smallest possible lattice size and the smallest possible number of discrete states, the results of which are as close as possible to the results of the Cellular Automaton with continuous state space. The Cellular Automaton with discrete state space evolves much faster than the Cellular Automaton with continuous state space. The state spaces of two Cellular Automata have been discretized using a Genetic Algorithm. The first Cellular Automaton simulates the two-dimensional photoresist etching process in integrated circuit fabrication and the second is used to predict forest fire spreading. A general method for the discretization of the state space of Cellular Automata using a Genetic Algorithm is also presented. The aim of this work is to provide a method for accelerating the execution of algorithms based on Cellular Automata (Cellular Automata algorithms) and to build a bridge between Cellular Automata as models for physical systems and processes and Cellular Automata as a VLSI architecture.
cellular automata genetic algorithms forest fire photolithography