Analysis of evolutionary process using evolutionary activity and modular schema analysis

Young Seol Lee, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A genetic algorithm is developed to find an optimal solution in a large search space using selection, crossover, and mutation. Some researchers have studied techniques for analysis of evolution process in genetic algorithm. In most cases, they were applied to only simple problem or they used schema theorem and numerical statistics to examine the process. These techniques are mostly developed because tracing schemas and interpreting semantics of the schemas require much effort and time. In this paper, we propose modular encoding of gene, which is used to facilitate the interpretation of the gene, identification of important parts of genetic code using evolutionary activity statistics, and analysis of the schemas. Also, we show the feasibility of the proposed method by tracing the evolution process of fuzzy robot controller.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
DOIs
Publication statusPublished - 2010 Dec 1
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 - Barcelona, Spain
Duration: 2010 Jul 182010 Jul 23

Publication series

Name2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

Other

Other2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
CountrySpain
CityBarcelona
Period10/7/1810/7/23

Fingerprint

Schema
Genes
Genetic algorithms
Statistics
Tracing
Semantics
Genetic Algorithm
Robots
Gene
Genetic Code
Controllers
Search Space
Crossover
Mutation
Encoding
Optimal Solution
Robot
Controller
Theorem

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

Lee, Y. S., & Cho, S. B. (2010). Analysis of evolutionary process using evolutionary activity and modular schema analysis. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 [5585996] (2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010). https://doi.org/10.1109/CEC.2010.5585996
Lee, Young Seol ; Cho, Sung Bae. / Analysis of evolutionary process using evolutionary activity and modular schema analysis. 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. 2010. (2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010).
@inproceedings{c69dac5c12d54acfb467143cfcfc112a,
title = "Analysis of evolutionary process using evolutionary activity and modular schema analysis",
abstract = "A genetic algorithm is developed to find an optimal solution in a large search space using selection, crossover, and mutation. Some researchers have studied techniques for analysis of evolution process in genetic algorithm. In most cases, they were applied to only simple problem or they used schema theorem and numerical statistics to examine the process. These techniques are mostly developed because tracing schemas and interpreting semantics of the schemas require much effort and time. In this paper, we propose modular encoding of gene, which is used to facilitate the interpretation of the gene, identification of important parts of genetic code using evolutionary activity statistics, and analysis of the schemas. Also, we show the feasibility of the proposed method by tracing the evolution process of fuzzy robot controller.",
author = "Lee, {Young Seol} and Cho, {Sung Bae}",
year = "2010",
month = "12",
day = "1",
doi = "10.1109/CEC.2010.5585996",
language = "English",
isbn = "9781424469109",
series = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010",
booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010",

}

Lee, YS & Cho, SB 2010, Analysis of evolutionary process using evolutionary activity and modular schema analysis. in 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010., 5585996, 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 10/7/18. https://doi.org/10.1109/CEC.2010.5585996

Analysis of evolutionary process using evolutionary activity and modular schema analysis. / Lee, Young Seol; Cho, Sung Bae.

2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. 2010. 5585996 (2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Analysis of evolutionary process using evolutionary activity and modular schema analysis

AU - Lee, Young Seol

AU - Cho, Sung Bae

PY - 2010/12/1

Y1 - 2010/12/1

N2 - A genetic algorithm is developed to find an optimal solution in a large search space using selection, crossover, and mutation. Some researchers have studied techniques for analysis of evolution process in genetic algorithm. In most cases, they were applied to only simple problem or they used schema theorem and numerical statistics to examine the process. These techniques are mostly developed because tracing schemas and interpreting semantics of the schemas require much effort and time. In this paper, we propose modular encoding of gene, which is used to facilitate the interpretation of the gene, identification of important parts of genetic code using evolutionary activity statistics, and analysis of the schemas. Also, we show the feasibility of the proposed method by tracing the evolution process of fuzzy robot controller.

AB - A genetic algorithm is developed to find an optimal solution in a large search space using selection, crossover, and mutation. Some researchers have studied techniques for analysis of evolution process in genetic algorithm. In most cases, they were applied to only simple problem or they used schema theorem and numerical statistics to examine the process. These techniques are mostly developed because tracing schemas and interpreting semantics of the schemas require much effort and time. In this paper, we propose modular encoding of gene, which is used to facilitate the interpretation of the gene, identification of important parts of genetic code using evolutionary activity statistics, and analysis of the schemas. Also, we show the feasibility of the proposed method by tracing the evolution process of fuzzy robot controller.

UR - http://www.scopus.com/inward/record.url?scp=79959455148&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79959455148&partnerID=8YFLogxK

U2 - 10.1109/CEC.2010.5585996

DO - 10.1109/CEC.2010.5585996

M3 - Conference contribution

AN - SCOPUS:79959455148

SN - 9781424469109

T3 - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

ER -

Lee YS, Cho SB. Analysis of evolutionary process using evolutionary activity and modular schema analysis. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. 2010. 5585996. (2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010). https://doi.org/10.1109/CEC.2010.5585996