7 research outputs found
Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata
We apply two evolutionary search algorithms: Particle Swarm Optimization
(PSO) and Genetic Algorithms (GAs) to the design of Cellular Automata (CA) that
can perform computational tasks requiring global coordination. In particular,
we compare search efficiency for PSO and GAs applied to both the density
classification problem and to the novel generation of 'chaotic' CA. Our work
furthermore introduces a new variant of PSO, the Binary Global-Local PSO
(BGL-PSO)
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 open-ended 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 Conway's 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.Comment: IEEE Symposium Series on Computational Intelligence 2019 (IEEE SSCI
2019
Asymptotic Behavior and ratios of Complexity in Cellular Automata
We study the asymptotic behavior of symbolic computing systems, notably one-dimensional cellular automata (CA), in order to ascertain whether and at what rate the number of complex versus simple rules dominate the rule space for increasing neighborhood range and number of symbols (or colors), and how different behavior is distributed in the spaces of different cellular automata formalisms. Using two different measures, Shannon's block entropy and Kolmogorov complexity, the latter approximated by two different methods (lossless compressibility and block decomposition), we arrive at the same trend of larger complex behavioral fractions. We also advance a notion of asymptotic and limit behavior for individual rules, both over initial conditions and runtimes, and we provide a formalization of Wolfram's classification as a limit function in terms of Kolmogorov complexity.</jats:p