Abstract
The relationship between complexity and various factors is explored through the simulation of the three neighbor ways of the game of life. It mainly discusses the state evolution process of cell populations under various evolutionary laws, various environmental scales and various initial states. Based on the discovery of a novel, long-lived and simple cell with an initial state, the periodic and stable cell morphology in Game of Life is introduced, thus reflecting the related complexity factors and changes. By simulating various environmental boundaries and comparing the steady-state graphs, it is concluded that a closed system will cause certain limitations in the final outcome. The limited environment will prevent the cell from expanding outward, but it can also create more periodic patterns. A limited environment is simultaneously an important factor in simplifying the system. In addition to the environment, the edge of chaos is also an important factor in the complexity of the system. An appropriate evolution rule can help the entire system find a balance in the chaos and present stable and interesting patterns. In addition, the correct neighbor method has a positive effect on the change of the cell. Finally, an infinite loop mode is set up to illustrate once again the wonder and complexity of Game of Life.
Highlights
Introduction to Cellular AutomataCellular automata is defined as a dynamic system which evolves in the discrete time dimension and a cell space composed of finite and discrete state cells in line with some system rules (Edwards & Maignan, 2020).It is a computational method based on computation or algorithm, which is used to compute and simulate natural phenomena in geometric space (Nandi et al, 1994)
It is in a discrete state in time and space, each variable corresponds to a certain amount of states, and the rules for changing states are local in space and time, so its state changes can be regarded as synchronous data processing (Chopard & Lagrava, 1999)
The closedness of the limited environment will increase the complexity of the system and affect the final layout of the cell group
Summary
Cellular automata is defined as a dynamic system which evolves in the discrete time dimension and a cell space composed of finite and discrete state cells in line with some system rules (Edwards & Maignan, 2020) It is a computational method based on computation or algorithm, which is used to compute and simulate natural phenomena in geometric space (Nandi et al, 1994). Cellular automata based on the rule computation provides new ideas and methods for simulating natural phenomena and life phenomena. It has a wide range of applications, such as simulating urban growth (Couclelis, 1997), and traffic conditions (Zhao & Xin, 2020). In the fourth part, the results of the program and influencing factors are simulated and discussed
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
