DocumentCode :
239181
Title :
Evolving multiplication as emergent behavior in cellular automata using conditionally matching rules
Author :
Bidlo, Michal
Author_Institution :
Fac. of Inf. Technol., Brno Univ. of Technol., Brno, Czech Republic
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
2732
Lastpage :
2739
Abstract :
In this paper a special representation technique called conditionally matching rules will be applied in order to design computational processes in uniform cellular automata. The goal is to verify abilities of this approach in combination with genetic algorithm on the problem of disigning various cellular automata that exhibit a given computational process. The principle of a computational process in a cellular automaton is to interpret some cells as input bits and some (possibly other) cells as output bits (i.e. the result of the computation). The genetic algorithm is applied to find a suitable transition function of a cellular automaton according to which the given computation could be observed during its development for all the possible binary combinations stored in the input cells. Both the input values and the result is represented by state values of cells. The input of the computation will be represented by the initial state of the cellular automaton. After a finite number of development steps the cells representing the output bits are expected to contain the result of the computation. A set of experiments will be performed considering various setups of the evolutionary system and arrangements of the target computation. It will be shown that non-trivial computations can be realized in a uniform two-dimensional cellular array.
Keywords :
cellular automata; evolutionary computation; genetic algorithms; pattern matching; cellular array; cellular automata; computational process; conditionally matching rules; emergent behavior; evolutionary system; evolving multiplication; genetic algorithm; matching rules; state values; target computation; uniform cellular automata; Automata; Boundary conditions; Cells (biology); Genetic algorithms; Sociology; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2014 IEEE Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6626-4
Type :
conf
DOI :
10.1109/CEC.2014.6900530
Filename :
6900530
Link To Document :
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