  Cellular automata are capable of developing complex behaviors based on simple
local interactions between their elements. Some of these characteristics have
been used to propose and improve meta-heuristics for global optimization;
however, the properties offered by the evolution rules in cellular automata
have not yet been used directly in optimization tasks. Inspired by the
complexity that various evolution rules of cellular automata can offer, the
continuous-state cellular automata algorithm (CCAA) is proposed. In this way,
the CCAA takes advantage of different evolution rules to maintain a balance
that maximizes the exploration and exploitation properties in each iteration.
The efficiency of the CCAA is proven with 33 test problems widely used in the
literature, 4 engineering applications that were also used in recent
literature, and the design of adaptive infinite-impulse response (IIR) filters,
testing 10 full-order IIR reference functions. The numerical results prove its
competitiveness in comparison with state-of-the-art algorithms. The source
codes of the CCAA are publicly available at
https://github.com/juanseck/CCAA.git
