AUTOMATIC GENERATION OF SELF-REPLICATING PATTERNS IN GRAPH AUTOMATA

2006 ◽  
Vol 16 (04) ◽  
pp. 1011-1018 ◽  
Author(s):  
KOHJI TOMITA ◽  
HARUHISA KUROKAWA ◽  
SATOSHI MURATA
Keyword(s):  
Evolutionary Computation ◽  
State Transition ◽  
Automatic Generation ◽  
Rule Generation ◽  
Graph Structures ◽  
Transition Rules ◽  
State Transition Rules

Graph automata define symbol dynamics of graph structures, which are capable of generating structures in addition to describing state transition. Rules of the graph automata are uniform and are therefore suitable for evolutionary computation. This paper shows that various self-replications are possible in graph automata, and that evolutionary computation is applicable to automatic rule generation to obtain self-replicating patterns of graph automata.

Simulating Self-Reproduction of Cells in a Two-Dimensional Cellular Automaton

2010 ◽  
Vol 22 (5) ◽  
pp. 669-676 ◽  
Author(s):  
Takeshi Ishida ◽  
Keyword(s):  
Synthetic Biology ◽  
Cellular Automaton ◽  
State Transition ◽  
Cellular Automaton Model ◽  
Molecular Machine ◽  
Two Dimensional ◽  
Transition Rules ◽  
State Transition Rules

Clarifying generalized self-reproduction is basic to applications in fields such as molecular machine production in nanotechnology and synthetic biology. The two-dimensional cellular automaton model we developed simulated cellular self-reproduction using a few state transition rules.

Binary Adaptive Ant Colony Optimization in Reactive Power Optimization

2012 ◽  
Vol 616-618 ◽  
pp. 2091-2096 ◽  
Author(s):  
Hong Hong ◽  
Fang Liu
Keyword(s):  
Ant Colony Optimization ◽  
State Transition ◽  
Reactive Power ◽  
Ant Colony ◽  
Reactive Power Optimization ◽  
Ant Colony Optimization Algorithm ◽  
Information Update ◽  
Transition Rules ◽  
Update Rules ◽  
State Transition Rules

This article proposed an Adaptive Binary Ant Colony Optimization Algorithm, which is based on the dual network diagram, designed to state transition rules and information update rules, and then according to the algorithm processes adjust information volatilizing factor dynamically, Verify the validity and superiority of the algorithm.

scholarly journals On discovering functions in actin filament automata

2019 ◽  
Vol 6 (1) ◽  
pp. 181198 ◽  
Author(s):  
Andrew Adamatzky
Keyword(s):  
Actin Filament ◽  
Resting State ◽  
Boolean Functions ◽  
State Transition ◽  
Input String ◽  
Time Interval ◽  
Refractory State ◽  
Transition Rules ◽  
State 1 ◽  
State Transition Rules

We simulate an actin filament as an automaton network. Every atom takes two or three states and updates its state, in discrete time, depending on a ratio of its neighbours in some selected state. All atoms/automata simultaneously update their states by the same rule. Two state transition rules are considered. In semi-totalistic Game of Life like actin filament automaton atoms take binary states ‘0’ and ‘1’ and update their states depending on a ratio of neighbours in the state ‘1’. In excitable actin filament automaton atoms take three states: resting, excited and refractory. A resting atom excites if a ratio of its excited neighbours belong to some specified interval; transitions from excited state to refractory state and from refractory state to resting state are unconditional. In computational experiments, we implement mappings of an 8-bit input string to an 8-bit output string via dynamics of perturbation/excitation on actin filament automata. We assign eight domains in an actin filament as I/O ports. To write True to a port, we perturb/excite a certain percentage of the nodes in the domain corresponding to the port. We read outputs at the ports after some time interval. A port is considered to be in a state True if a number of excited nodes in the port's domain exceed a certain threshold. A range of eight-argument Boolean functions is uncovered in a series of computational trials when all possible configurations of eight-elements binary strings were mapped onto excitation outputs of the I/O domains.

Path planning of mobile robot based on adaptive ant colony algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5329-5338
Author(s):  
Yan Zheng ◽  
Qiang Luo ◽  
Haibao Wang ◽  
Changhong Wang ◽  
Xin Chen
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
State Transition ◽  
Ant Colony ◽  
Local Optimum ◽  
Heuristic Function ◽  
State Transfer ◽  
Transition Rules ◽  
Adaptive Heuristic ◽  
State Transition Rules

The traditional ant colony algorithm has some problems, such as low search efficiency, slow convergence speed and local optimum. To solve those problems, an adaptive heuristic function is proposed, heuristic information is updated by using the shortest actual distance, which ant passed. The reward and punishment rules are introduced to optimize the local pheromone updating strategy. The state transfer function is optimized by using pseudo-random state transition rules. By comparing with other algorithms’ simulation results in different simulation environments, the results show that it has effectiveness and superiority on path planning.

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