Detail publikace

Evolution of Cellular Automata Using Instruction-Based Approach

Originální název

Evolution of Cellular Automata Using Instruction-Based Approach

Anglický název

Evolution of Cellular Automata Using Instruction-Based Approach

Jazyk

en

Originální abstrakt

This paper introduces a method of encoding cellular automata local transition function using an instruction-based approach and their design by means of genetic algorithms. The proposed method represents an indirect mapping between the input combinations of states in the cellular neighborhood and the next states of the cells during the development steps. In this case the local transition function is described by a program (algorithm) whose execution calculates the next cell states. The objective of the program-based representation is to reduce the length of the chromosome in case of the evolutionary design of cellular automata. It will be shown that the instruction-based development allows us to design complex cellular automata with higher success rate than the conventional table-based method especially for complex cellular automata with more than two cell states. The case studies include the replication problem and the problem of development of a given pattern from an initial seed.

Anglický abstrakt

This paper introduces a method of encoding cellular automata local transition function using an instruction-based approach and their design by means of genetic algorithms. The proposed method represents an indirect mapping between the input combinations of states in the cellular neighborhood and the next states of the cells during the development steps. In this case the local transition function is described by a program (algorithm) whose execution calculates the next cell states. The objective of the program-based representation is to reduce the length of the chromosome in case of the evolutionary design of cellular automata. It will be shown that the instruction-based development allows us to design complex cellular automata with higher success rate than the conventional table-based method especially for complex cellular automata with more than two cell states. The case studies include the replication problem and the problem of development of a given pattern from an initial seed.

BibTex


@inproceedings{BUT96927,
  author="Michal {Bidlo} and Zdeněk {Vašíček}",
  title="Evolution of Cellular Automata Using Instruction-Based Approach",
  annote="This paper introduces a method of encoding cellular automata local transition
function using an instruction-based approach and their design by means of genetic
algorithms. The proposed method represents an indirect mapping between the input
combinations of states in the cellular neighborhood and the next states of the
cells during the development steps. In this case the local transition function is
described by a program (algorithm) whose execution calculates the next cell
states. The objective of the program-based representation is to reduce the length
of the chromosome in case of the evolutionary design of cellular automata. It
will be shown that the instruction-based development allows us to design complex
cellular automata with higher success rate than the conventional table-based
method especially for complex cellular automata with more than two cell states.
The case studies include the replication problem and the problem of development
of a given pattern from an initial seed.",
  address="Institute of Electrical and Electronics Engineers",
  booktitle="2012 IEEE World Congress on Computational Intelligence",
  chapter="96927",
  doi="10.1109/CEC.2012.6256475",
  edition="NEUVEDEN",
  howpublished="online",
  institution="Institute of Electrical and Electronics Engineers",
  year="2012",
  month="january",
  pages="1060--1067",
  publisher="Institute of Electrical and Electronics Engineers",
  type="conference paper"
}