Evolved Cellular Automata for Edge Detection in Binary Images One significant and complex image processing task that may be used as a step in various complex processes is the edge detection. The mechanism of detecting the edges is called edge detector. The scope of this paper is to propose an edge detector based on evolved Cellular Automata (CA), for binary images. It was proved in many cases that CA may be successfully applied in various image processing tasks because they have a number of advantages over the traditional methods of computations: simplicity of implementation, the complexity of behaviour, parallelisation, extensibility, scalability, robustness. In the scope of this paper, two Evolutionary Algorithms are used to evolve the CAxe2x80x99 rule to detect edge points in binary images.

Evolving Structures in Complex Systems In this paper we propose an approach for measuring growth of complexity of emerging patterns in complex systems such as cellular automata. We discuss several ways how a metric for measuring the complexity growth can be defined. This includes approaches based on compression algorithms and artificial neural networks. We believe such a metric can be useful for designing systems that could exhibit openended evolution, which itself might be a prerequisite for development of general artificial intelligence. We conduct experiments on 1D and 2D grid worlds and demonstrate that using the proposed metric we can automatically construct computational models with emerging properties similar to those found in the Conwayu0027s Game of Life, as well as many other emergent phenomena. Interestingly, some of the patterns we observe resemble forms of artificial life. Our metric of structural complexity growth can be applied to a wide range of complex systems, as it is not limited to cellular automata.