Computation at the edge of chaos: phase transitions and emergent computation
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Evolving cellular automata to perform computations: mechanisms and impediments
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Open problems in artificial life
Artificial Life - Special issue on the Artificial Life VII: looking backward, looking forward
ARC'07 Proceedings of the 3rd international conference on Reconfigurable computing: architectures, tools and applications
Hi-index | 0.00 |
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.