Details
| Original language | English |
|---|---|
| Title of host publication | Information Science and Applications 2018 |
| Subtitle of host publication | ICISA 2018 |
| Editors | Kuinam J. Kim, Nakhoon Baek |
| Publisher | Springer Singapore |
| Pages | 369-375 |
| Number of pages | 7 |
| Edition | 1. |
| ISBN (electronic) | 978-981-13-1056-0 |
| ISBN (print) | 978-981-13-4557-9, 978-981-13-1055-3 |
| Publication status | Published - 24 Jul 2019 |
| Event | International Conference on Information Science and Applications, ICISA 2018 - Kowloon, Hong Kong Duration: 25 Jun 2018 → 27 Jun 2018 |
Publication series
| Name | Lecture Notes in Electrical Engineering |
|---|---|
| Volume | 514 |
| ISSN (Print) | 1876-1100 |
| ISSN (electronic) | 1876-1119 |
Abstract
In this work we improve previous approaches based on genetic algorithms (GA) to solve sudoku puzzles. Those approaches use random swap mutations and filtered mutations, where both operations result in relatively slow convergence, the latter suffering a bit less. We suggest to improve GA based approaches by an intermediate local optimization step of the population. Compared to the previous approaches our approach is superior in terms of convergence rate, success rate and speed. As consequence we find the optimum with one population member and within one generation in a few milliseconds instead of nearly one minute.
Keywords
- Criticism, Genetic algorithms, Sudoku
ASJC Scopus subject areas
- Engineering(all)
- Industrial and Manufacturing Engineering
Cite this
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Information Science and Applications 2018: ICISA 2018. ed. / Kuinam J. Kim; Nakhoon Baek. 1. ed. Springer Singapore, 2019. p. 369-375 (Lecture Notes in Electrical Engineering; Vol. 514).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Improving an evolutionary approach to Sudoku Puzzles by intermediate optimization of the population
AU - Becker, Matthias
AU - Balci, Sinan
PY - 2019/7/24
Y1 - 2019/7/24
N2 - In this work we improve previous approaches based on genetic algorithms (GA) to solve sudoku puzzles. Those approaches use random swap mutations and filtered mutations, where both operations result in relatively slow convergence, the latter suffering a bit less. We suggest to improve GA based approaches by an intermediate local optimization step of the population. Compared to the previous approaches our approach is superior in terms of convergence rate, success rate and speed. As consequence we find the optimum with one population member and within one generation in a few milliseconds instead of nearly one minute.
AB - In this work we improve previous approaches based on genetic algorithms (GA) to solve sudoku puzzles. Those approaches use random swap mutations and filtered mutations, where both operations result in relatively slow convergence, the latter suffering a bit less. We suggest to improve GA based approaches by an intermediate local optimization step of the population. Compared to the previous approaches our approach is superior in terms of convergence rate, success rate and speed. As consequence we find the optimum with one population member and within one generation in a few milliseconds instead of nearly one minute.
KW - Criticism
KW - Genetic algorithms
KW - Sudoku
UR - http://www.scopus.com/inward/record.url?scp=85051077190&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-1056-0_38
DO - 10.1007/978-981-13-1056-0_38
M3 - Conference contribution
AN - SCOPUS:85051077190
SN - 978-981-13-4557-9
SN - 978-981-13-1055-3
T3 - Lecture Notes in Electrical Engineering
SP - 369
EP - 375
BT - Information Science and Applications 2018
A2 - Kim, Kuinam J.
A2 - Baek, Nakhoon
PB - Springer Singapore
T2 - International Conference on Information Science and Applications, ICISA 2018
Y2 - 25 June 2018 through 27 June 2018
ER -