Synthesis of self-replication cellular automata using genetic algorithms

被引：1
|
作者：
Kajisha, H [1 ]
Saito, T [1 ]
机构：
[1] Hosei Univ, EEE Dept, Tokyo 1848584, Japan
来源：
IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL V | 2000年
关键词：
D O I：
10.1109/IJCNN.2000.861453
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents an efficient searching algorithm for one-dimensional cellular automata (CAs) with self-replicating structure. In the algorithm, the CA structure is represented by a simple fitness function and a genetic algorithm is used effectively where a gene implies a rule table. Based on preliminary experimental results, we provide interesting conjectures: 1) There exists optimal mutation rate for the fitness evolution, and 2) If genes are evolved successfully, they can produce some typical patterns.
引用
收藏
页码：173 / 177
页数：5
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