Since then, cellular automata have become an important tool for studying, solving and applying complex systems. Researchers have successfully discovered the phenomena of bifurcation, attractor, self-replication and self-organization by using the existing simple rules. In the 1980s, there was a new breakthrough in the theoretical research of cellular automata. Therefore, it is difficult to be implemented. Von Neumann first proposed the concept of cellular automata, so as to study the phenomenon of self-replication of machines, which is a computer model describing the process of reproduction and evolution, but it is too complicated, with thousands of cells and 29 states. This method can abstract all the details of the basic physical system, but is still faithful to the basic physical reality it describes. For example, cellular automata are proved to be an abstract computational mathematical model, and cellular automata can be used as models or theories for physical systems and equipment. Any model that satisfies these rules can be regarded as a cellular automata model.
#VISUAL AUTOMATA SIMULATOR SERIES#
Different from general dynamic models, cellular automata are not determined by strictly defined physical equations or functions, but are constituted by a series of model construction rules. A large number of cells form the evolution of a dynamic system through simple interaction. Each cell scattered in the regular grid takes a finite discrete state, follows the same action rules, and updates synchronously according to certain local rules. It is a dynamic system that is discrete in time and space. They consist of some cells, which will change over time according to specific rules. Cellular Automata are discrete models which are now widely used in scientific simulations and researches. The spatial model composed of a series of cells with the same properties is called cellular automata.
The game of life enlightens us: the simplest logical rules can produce complex and interesting activities, and a complex system may be iterated by simple rules. These structures interact in complex ways to understand the concept of “whole and part”. In the process of change, some local structures remain fixed, and some local structures present periodic cycles. By setting a random initial state to make the chaotic and disorderly situation simple, the concept of “determinism and randomness” can be better expressed. I introduce random quantities into the system to make the simulation results closer to the actual situation. Meanwhile, the evolution process of symmetrical initial state is also symmetrically distributed. The entire system is completely closed and has certain limitations. It is also concluded that a suitable initial state can reach the final state in fewer steps, which can greatly simplify the evolution process.
#VISUAL AUTOMATA SIMULATOR CODE#
Setting different initial states through the code and observing the final generated graphics, you can see that the complex and simple initial states can achieve the same result. According to cell forms such as circulation and disappearance, it reflects the complex changes of Game of Life. Vs2010 is used as the development environment, so as to realize the visual programming of Game of Life, and explore the life evolution process of cell group in different sizes and states.