2011
Martínez-Molina, Moreno-Armendáriz, M. A., Cruz-Cortés, N., & Seck-Tuoh-Mora, J. C. (2011). Modeling Prey-Predator Dynamics via Particle Swarm Optimization and Cellular Automata. In Batyrshin, I., & Sidorov, G. (Eds.), Advances in Soft Computing, Lecture Notes in Computer Science 7095, Springer Berlin Heidelberg.
Abstract
Through the years several methods have been used tomodel organisms movement within an ecosystem modelled with cellular automata, from simple algorithms that change cells state according to some pre-defined heuristic, to diffusion algorithms based on the one dimensional Navier - Stokes equation or lattice gases. In this work we show a novel idea since the predator dynamics evolve through Particle Swarm Optimization.
Unconventional invertible behaviors in reversible one-dimensional cellular automata.
Pair Diagram and Cyclic Properties Characterizing the Inverse of Reversible Automata
Reproducing the Cyclic Tag System Developed by Matthew Cook with Rule 110 Using the Phases f(i-)1.
Complex Dynamics Emerging in Rule 30 with Majority Memory
How to Make Dull Cellular Automata Complex by Adding Memory: Rule 126 Case Study
Elementary cellular automaton Rule 110 explained as a block substitution system
Modeling a Nonlinear Liquid Level System by Cellular Neural Networks