ABSTRACT
In this work we study open-ended evolution through the analysis of a new model, HetCA, for "heterogeneous cellular automata". Striving for simplicity, HetCA is based on classical two-dimensional CA, but differs from them in several key ways: cells include properties of "age", "decay", and "quiescence"; cells utilize a heterogeneous transition function, one inspired by genetic programming; and there exists a notion of genetic transfer between adjacent cells. The cumulative effect of these changes is the creation of an evolving ecosystem of competing cell colonies. To evaluate the results of our new model, we define a measure of phenotypic diversity on the space of cellular automata. Via this measure, we contrast HetCA to several controls known for their emergent behaviours---homogeneous CA and the Game of Life---and several variants of our model. This analysis demonstrates that HetCA has a capacity for long-term phenotypic dynamics not readily achieved in other models. Runs exceeding one million time steps do not exhibit stagnation or even cyclic behaviour. Further, we show that the design choices are well motivated, as the exclusion of any one of them disrupts the long-term dynamics.
- C. Adami, C. Brown, and W. Kellogg. Evolutionary learning in the 2D artificial life system "Avida". In Artificial Life IV, pages 377--381, 1994.Google Scholar
- M. Bidlo and Z. Vašíček. Cellular automata-based development of combinational and polymorphic circuits: A comparative study. In G. Hornby, L. Sekanina, and P. Haddow, editors, Evolvable Systems: From Biology to Hardware, pages 106--117. Springer, 2008. Google ScholarDigital Library
- M. Bidlo and Z. Vašíček. Evolution of cellular automata using instruction-based approach. In IEEE Congress on Evolutionary Computation (CEC), pages 1--8, 2012.Google ScholarCross Ref
- M. Brameier and W. Banzhaf. Linear Genetic Programming. Springer, 2006. Google ScholarDigital Library
- A. Channon. Unbounded evolutionary dynamics in a system of agents that actively process and transform their environment. Genetic Programming and Evolvable Machines, 7:253--281, 2006. Google ScholarDigital Library
- J. Conway, R. Guy, and E. Berlekamp. Winning Ways for Your Mathematical Plays, Vol. 2. CRC Press, 2003.Google Scholar
- A. Deutsch and S. Dormann. Cellular Automaton Modelling of Biological Pattern Formation: Characterization, Applications and Analysis. Birkhauser, 2005.Google Scholar
- A. Dorin. The virtual ecosystem as generative electronic art. In G. R. et. al., editor, European Workshop on Evolutionary Music and Art, Applications of Evolutionary Computing (EvoWorkshops), pages 467--476, 2004.Google Scholar
- R. Doursat. The growing canavas of biological development: multiscale pattern generation on an expanding lattice of gene regulatory networks. InterJournal: Complex Systems, 1809, 2006.Google Scholar
- D. Fogel. Nils Barricelli - artificial life, coevolution, self-adaptation. IEEE Computational Intelligence Magazine, 1(1):41--45, 2006. Google ScholarDigital Library
- N. Ganguly, B. Sikdar, A. Deutsch, G. Canright, and P. Chaudhuri. A survey on cellular automata. Technical Report 9, Centre for High Performance Computing, Dresden University of Technology, 2003.Google Scholar
- A. Ilachinski. Cellular Automata: A Discrete Universe. World Scientific, 2001. Google ScholarDigital Library
- T. Kowaliw and W. Banzhaf. Augmenting artificial development with local fitness. In IEEE Congress on Evolutionary Computation (CEC), pages 316--323, 2009. Google ScholarDigital Library
- T. Kowaliw, A. Dorin, and J. McCormack. Promoting creative design in interactive evolutionary computation. IEEE Transactions on Evolutionary Computation, 16(4):523--536, 2012.Google ScholarDigital Library
- T. Kowaliw, P. Grogono, and N. Kharma. Bluenome: A novel developmental model of artificial morphogenesis. In K. D. et al., editor, 6th Conference on Genetic and Evolutionary Computation (GECCO), 2004.Google Scholar
- T. Kowaliw, P. Grogono, and N. Kharma. The evolution of structural form through artificial embryogeny. In IEEE Symposium on Artificial Life (IEEE-ALIFE), pages 425--432. IEEE, 2007.Google Scholar
- J. Miller. Evolving a self-repairing, self-regulating, french flag organism. In K. D. et al., editor, 6th Conference on Genetic and Evolutionary Computation (GECCO), pages 129--139. Springer-Verlag, 2004.Google Scholar
- Z. Pan and J. Reggia. Computational discovery of instructionless self-replicating structures in cellular automata. Artificial Life, 16:1064--5462, 2010. Google ScholarDigital Library
- R. Poli, W. Langdon, and N. McPhee. A Field Guide To Genetic Programming. Lulu Enterprises, 2008. Google ScholarDigital Library
- T. Ray. Evolution, ecology and optimization of digital organisms. Santa Fe Institute Technical Report 92-08--942, 1992.Google Scholar
- M. Rönkkö. An artificial ecosystem: Emergent dynamics and lifelike properties. Artificial Life, 13(2):159--187, 2007. Google ScholarDigital Library
- H. Sayama. Self-protection and diversity in self-replicating cellular automata. Artificial Life, 10(1):83--98, 2004. Google ScholarDigital Library
- M. Sipper. Computing with cellular automata: Three cases for nonuniformity. Phys. Rev. E, 57:3589--3592, Mar 1998.Google ScholarCross Ref
- M. Sipper and M. Tomassini. Computation in artificially evolved, non-uniform cellular automata. Theoretical Computer Science, 217(1):81--98, 1999. Google ScholarDigital Library
- G. Tufte. Gene regulation mechanisms introduced in the evaluation criteria for a hardware cellular development system. In NASA/ESA Conference on Adaptive Hardware and Systems, pages 137--144, 2006. Google ScholarDigital Library
- G. Vichniac, P. Tamayo, and H. Hartman. Annealed and quenched inhomogeneous cellular automata (INCA). Journal of Statistical Physics, 45:875--883, 1986.Google ScholarCross Ref
- S. Wolfram. A New Kind of Science. Wolfram Media Inc., 2002. Google ScholarDigital Library
- L. Yaeger. Computational genetics, physiology, metabolism, neural systems, learning, vision, and behavior or polyworld: Life in a new context. In Artificial Life III, pages 263--298, 1993.Google Scholar
- A. Yinusa and C. Nehaniv. Study of inheritable mutations in von Neumann self-reproducing automata using the golly simulator. In 2011 Symposium on Artificial Life (ALIFE), pages 211--217, 2011.Google ScholarCross Ref
Index Terms
- Long-term evolutionary dynamics in heterogeneous cellular automata
Recommendations
Cellular Automata: Elementary Cellular Automata
Cellular automata CA are discrete dynamical systems consist of a regular finite grid of cell; each cell encapsulating an equal portion of the state, and arranged spatially in a regular fashion to form an n-dimensional lattice. A cellular automata is ...
Descriptional complexity of cellular automata and decidability questions
Third international workshop on descriptional complexity of automata, grammars and related structuresWe study the descriptional complexity of cellular automata (CA) which are a parallel model of computation. We show that between one of the simplest cellular models, the realtime one-way CA (realtime-OCA), and "classical" models like deterministic finite ...
Closure properties of cellular automata
Concerning the power of one-dimensional cellular automata recognizers, Ibarra and Jiang have proved that real time cellular automata (CA) and linear time CA are equivalent if and only if real time CA is closed under reverse. In this paper we investigate ...
Comments