A key concern in artificial-life-oriented research in complex systems
has been the relationship between the dynamical behaviour of cellular
automata
(CA) and their computational ability. Along this line,
evolutionary methods have been used to look for CA with predefined
computational behaviours, the most widely studied task having been the
Density Classification Task
(DCT). It has recently been showed that
the use of an heuristic guided by parameters that estimate the
dynamical behaviour of CA, can improve evolutionary search. On the
other hand, an approach that has been successfully applied to several
kinds of problems is the Evolutionary Multiobjective Optimization
(EMOO). Here, the EMOO technique called Non-Dominated Sorting Genetic
Algorithm is combined with the parameter-based heuristic, and
successfullly applied to the DCT, suggesting a positive synergy out of
using the two techniques in the search for CA.
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