Adaptability and Homeostasis in the Game of Life interacting with the evolved Cellular Automata

2012 ◽  
pp. 232-254
Author(s):  
Keisuke Suzuki ◽  
Takashi Ikegami
Keyword(s):  
Genetic Algorithm ◽  
Cellular Automata ◽  
Space Time ◽  
Layer System ◽  
Game Of Life ◽  
Time Dynamic ◽  
State 1 ◽  
Number Of Cells

In this paper, the authors study the emergence of homeostasis in a two-layer system of the Game of Life, in which the Game of Life in the first layer couples with another system of cellular automata in the second layer. Homeostasis is defined as a space-time dynamic that regulates the number of cells in state-1 in the Game of Life layer. A genetic algorithm is used to evolve the rules of the second layer to control the pattern of the Game of Life. The authors found that two antagonistic attractors control the numbers of cells in state-1 in the first layer. The homeostasis sustained by these attractors is compared with the homeostatic dynamics observed in Daisy World.

Adaptability and Homeostasis in the Game of Life interacting with the evolved Cellular Automata

2010 ◽  
Vol 1 (3) ◽  
pp. 40-50
Author(s):  
Keisuke Suzuki ◽  
Takashi Ikegami
Keyword(s):  
Genetic Algorithm ◽  
Cellular Automata ◽  
Space Time ◽  
Layer System ◽  
Game Of Life ◽  
Time Dynamic ◽  
State 1 ◽  
Number Of Cells

In this paper, the authors study the emergence of homeostasis in a two-layer system of the Game of Life, in which the Game of Life in the first layer couples with another system of cellular automata in the second layer. Homeostasis is defined as a space-time dynamic that regulates the number of cells in state-1 in the Game of Life layer. A genetic algorithm is used to evolve the rules of the second layer to control the pattern of the Game of Life. The authors found that two antagonistic attractors control the numbers of cells in state-1 in the first layer. The homeostasis sustained by these attractors is compared with the homeostatic dynamics observed in Daisy World.

scholarly journals OPTIMIZATION OF 2D LATTICE CELLULAR AUTOMATA FOR PSEUDORANDOM NUMBER GENERATION

2005 ◽  
Vol 16 (03) ◽  
pp. 479-500 ◽  
Author(s):  
MARIE THERESE ROBLES QUIETA ◽  
SHENG-UEI GUAN
Keyword(s):  
Genetic Algorithm ◽  
Cellular Automata ◽  
Neighborhood Characteristics ◽  
Structure And Properties ◽  
Pseudorandom Number Generation ◽  
The Cost ◽  
Number Of Cells ◽  
Better Than ◽  
2D Array

This paper proposes a generalized approach to 2D CA PRNGs — the 2D lattice CA PRNG — by introducing vertical connections to arrays of 1D CA. The structure of a 2D lattice CA PRNG lies in between that of 1D CA and 2D CA grid PRNGs. With the generalized approach, 2D lattice CA PRNG offers more 2D CA PRNG variations. It is found that they can do better than the conventional 2D CA grid PRNGs. In this paper, the structure and properties of 2D lattice CA are explored by varying the number and location of vertical connections, and by searching for different 2D array settings that can give good randomness based on Diehard test. To get the most out of 2D lattice CA PRNGs, genetic algorithm is employed in searching for good neighborhood characteristics. By adopting an evolutionary approach, the randomness quality of 2D lattice CA PRNGs is optimized. In this paper, a new metric, #rn is introduced as a way of finding a 2D lattice CA PRNG with the least number of cells required to pass Diehard test. Following the introduction of the new metric #rn, a cropping technique is presented to further boost the CA PRNG performance. The cost and efficiency of 2D lattice CA PRNG is compared with past works on CA PRNGs.

scholarly journals MPI implementations of Conways Game of Life and Kohomoto-Oono cellular automata

2010 ◽  
Vol 2 (3) ◽  
pp. 319-322
Author(s):  
Anna Yur'evna Subbotina ◽  
Nikolai Igorevich Khokhlov
Keyword(s):  
Cellular Automata ◽  
Game Of Life
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