References
- Wolfram S. Cellular automata and complexity: collected papers. Vol. 1. Reading (MA): Addison-Wesley; 1994.
- Oliveira GMB, Bortot JC, De Oliveira PP. Multiobjective evolutionary search for one-dimensional cellular automata in the density classification task. In: Artificial Life VIII: The 8th International Conference on Artificial Life. Sydney, Australia; 2003. p. 202–206.
- Wolfram S. Cryptography with cellular automata. In: Conference on the Theory and Application of Cryptographic Techniques. Linz, Austria; 1985. p. 429–432.
- Seredynski F, Zomaya AY. Sequential and parallel cellular automata-based scheduling algorithms. IEEE Trans Parallel Distrib Syst. 2002;13:1009–1022.
- Carvalho TI, Carneiro MG, Oliveira GMB. A comparison of a proposed dynamical direct verification of lattice’s configuration and a forecast behavior parameter on a cellular automata model to task scheduling. In: International Conference on Cellular Automata. Fez, Marocco; 2016. p. 258–268.
- Carneiro MG, Oliveira GMB. Synchronous cellular automata-based scheduler initialized by heuristic and modeled by a pseudo-linear neighborhood. Nat Comput. 2013;12:339–351.
- Carvalho TI, Oliveira GMB. Searching for non-regular neighborhood cellular automata rules applied to scheduling task and guided by a forecast dynamical behavior parameter. In: Proceedings of ECAL. York, England; 2015. p. 538–545.
- Agrawal P, Rao S. Energy-aware scheduling of distributed systems. IEEE Trans Autom Sci Eng. 2014;11:1163–1175.
- Carneiro MG, Oliveira GMB. Scas-h: synchronous cellular automata-based scheduler with initialization heuristic to task scheduling. Autom JAC. 2012;2012:52.
- Carvalho TI, Carneiro MG, Oliveira GMB. A hybrid strategy to evolve cellular automata rules with a desired dynamical behavior applied to the task scheduling problem. In: 5th Brazilian Conference on Intelligent Systems (BRACIS). Recife, Brazil; 2016. p. 492–497.
- Wolfram S. A new kind of science. Champaign (IL): Wolfram Media Inc.; 2002.
- Li W, Packard N. The structure of the elementary cellular automata rule space. Complex Syst. 1990;4:281–297.
- Binder PM. Parametric ordering of complex systems. Phys Rev E. 1994;49:2023–2030.
- Kwok YK, Ahmad I. Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput Surv. 1999;31:406–471.
- Pinedo ML. Scheduling: theory, algorithms, and systems. 3rd ed. New York (NY): Springer Science; 2008.
- Garey MR, Johnson DS. Computers and interactability. A guide to the theory of NPCompleteness. New York (NY): Freeman and Company; 1979.
- Mitchell M. Computation in cellular automata: a selected review. In: Nonstandard computation. Weinheim, Germany: VCH Verlagsgesellschaft; 1996. p. 95–140.
- Fates N. Stochastic cellular automata solutions to the density classification problem. Theory Comput Syst. 2013;53:223–242.
- Oliveira GMB, Martins LG, Carvalhode LB, et al. Some investigations about synchronization and density classification tasks in one-dimensional and two-dimensional cellular automata rule spaces. Electron Notes Theor Comput Sci. 2009;252:121–142.
- Wolnik B, Dembowski M, Bołt W, et al. The density classification problem in the context of continuous cellular automata. In: International Conference on Cellular Automata. Fez, Marocco; 2016. p. 79–87.
- Jin S, Schiavone G, Turgut D. A performance study of multiprocessor task scheduling algorithms. J Supercomput. 2008;43:77–97.
- Swiecicka A, Seredynski F, Zomaya AY. Multiprocessor scheduling and rescheduling with use of cellular automata and artificial immune system support. IEEE Trans Parallel Distrib Syst. 2006;17:253–262.
- Carneiro MG, Oliveira GMB. Cellular automata-based model with synchronous updating for task static scheduling. In: Proceedings of 17th International Workshop on Cellular Automata and Discrete Complex System. Santiago, Chile; 2011. p. 263–272.
- Carneiro MG, Oliveira GMB. SCAS-IS: knowledge extraction and reuse in multiprocessor task scheduling based on cellular automata. In: Proceedings of Brazilian Symposium on Neural Networks (SBRN). Curitiba, Brazil; 2012. p. 142–147.
- Vidica PM, Oliveira GMB. Cellular automata-based scheduling: a new approach to improve generalization ability of evolved rules. In: Ninth Brazilian symposium on neural networks (SBRN’06). Ribeirão Petro, Brazil: IEEE; 2006. p. 18–23.
- Oliveira GM, Vidica PM. A coevolutionary approach to cellular automata-based task scheduling. In: Sirakoulis GC, Bandini S, editors. Cellular automata. Vol. 7495, Lecture Notes in Computer Science. Berlin: Springer; 2012. p. 111–120.
- Ghafarian T, Deldari H, Akbarzadeh-T MR. Multiprocessor scheduling with evolving cellular automata based on ant colony optimization. In: Computer Conference, 2009. CSICC 2009. 14th International CSI. Tehran, Iran; 2009. p. 431–436.
- Boutekkouk F. A cellular automaton based approach for real time embedded systems scheduling problem resolution. In: Silhavy R, Senkerik R, Oplatkova ZK,et al. , editors. Proceedings of the 4th computer science on-line conference 2015 (CSOC2015). New York (NY): Springer; 2015. p. 13–22.
- Kucharska E, Grobler-Debska K, Raczka K, et al. Cellular automata approach for parallel machine scheduling problem. Simulation. 2016;92:165–178.
- Cosnard M, Marrakchi M, Robert Y, et al. Parallel Gaussian elimination on an MIMD computer. Parallel Comput. 1988;6:275–296.
- DAG generation program. 2011. Available from: http://www.loria.fr/~suter/dags.html.
- Olteanu A, Marin A. Generation and evaluation of scheduling dags: how to provide similar evaluation conditions. Comput Sci Master Res. 2011;1:57–66.