Modeling Artificial Life: A Cellular Automata Approach

Abstract

The key feature of artificial life is the idea of emergence, where new patterns or behaviors emerge from complex computational processes that cannot be predicted. Emergence initiates the formation of higher-order properties via the interaction of lower-level properties. Biological networks contain many theory models of evolution. Similarities between the theoretically estimated networks and empirically modeled counterpart networks are considered as evidence of the theoretic and predictive biological evolution. However, the methods by which these theoretical models are parameterized and modeled might lead to inference validity questions. Opting for randomized parametric values is a probabilistic concern that a model produces. There persists a wide range of probable parameter values which allow a model to produce varying statistic results according to the parameters selected. While using the phenomenon of cellular automata, we tried to model life on a grid of squares. Each square in the grid is taken as a biological cell; we have framed rules such that the process of cell division and pattern formation in terms of biological theoretic perspective is studied. Relatively complex behaviors of the cell patterns which vary from generation to generation are visually analyzed. Three algorithms—game of life, Langton’s ant, and hodgepodge—have been implemented whose technical implementation will provide an inspiration and foundation to build simulators that exhibit characteristics and behaviors of biological systems of reproduction.