Detail publikace

Investigation of Replicating Tiles in Cellular Automata Designed by Evolution Using Conditionally Matching Rules

Originální název

Investigation of Replicating Tiles in Cellular Automata Designed by Evolution Using Conditionally Matching Rules

Anglický název

Investigation of Replicating Tiles in Cellular Automata Designed by Evolution Using Conditionally Matching Rules

Jazyk

en

Originální abstrakt

In this paper we investigate the evolutionary design of replicating tiles in cellular automata. In particular, various sizes of the tiles will be considered whose replication ought to be performed by satisfying a given arrangement of the tiles with respect to each other. The goal is to determine the abilities of the genetic algorithm in combination with conditionally matching rules used for representing the transition functions of cellular automata to find solutions for tiles consisting of up to a hundred of cells. A comparative study will be presented considering the success rate, computational effort and complexity of the obtained solutions as the main values of interest. It will be shown that, in addition to the tile size and the number of states of the cellular automaton, the probability of finding a correct solution is also substantially influenced by the arrangement style. The results show that the tile arrangement that may be considered as the simplest one does not have to necessarily be easily realisable by the genetic algorithm as a transition function for a cellular automaton.

Anglický abstrakt

In this paper we investigate the evolutionary design of replicating tiles in cellular automata. In particular, various sizes of the tiles will be considered whose replication ought to be performed by satisfying a given arrangement of the tiles with respect to each other. The goal is to determine the abilities of the genetic algorithm in combination with conditionally matching rules used for representing the transition functions of cellular automata to find solutions for tiles consisting of up to a hundred of cells. A comparative study will be presented considering the success rate, computational effort and complexity of the obtained solutions as the main values of interest. It will be shown that, in addition to the tile size and the number of states of the cellular automaton, the probability of finding a correct solution is also substantially influenced by the arrangement style. The results show that the tile arrangement that may be considered as the simplest one does not have to necessarily be easily realisable by the genetic algorithm as a transition function for a cellular automaton.

BibTex


@inproceedings{BUT119881,
  author="Michal {Bidlo}",
  title="Investigation of Replicating Tiles in Cellular Automata Designed by Evolution Using Conditionally Matching Rules",
  annote="In this paper we investigate the evolutionary design of replicating tiles in
cellular automata. In particular, various sizes of the tiles will be considered
whose replication ought to be performed by satisfying a given arrangement of the
tiles with respect to each other. The goal is to determine the abilities of the
genetic algorithm in combination with conditionally matching rules used for
representing the transition functions of cellular automata to find solutions for
tiles consisting of up to a hundred of cells. A comparative study will be
presented considering the success rate, computational effort and complexity of
the obtained solutions as the main values of interest. It will be shown that, in
addition to the tile size and the number of states of the cellular automaton, the
probability of finding a correct solution is also substantially influenced by the
arrangement style. The results show that the tile arrangement that may be
considered as the simplest one does not have to necessarily be easily realisable
by the genetic algorithm as a transition function for a cellular automaton.",
  address="IEEE Computational Intelligence Society",
  booktitle="2015 IEEE International Conference on Evolvable Systems (ICES)",
  chapter="119881",
  doi="10.1109/SSCI.2015.213",
  edition="Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence (SSCI)",
  howpublished="electronic, physical medium",
  institution="IEEE Computational Intelligence Society",
  year="2015",
  month="december",
  pages="1506--1513",
  publisher="IEEE Computational Intelligence Society",
  type="conference paper"
}