Abstract
This paper proposes an acceleration method of GA search that finds a new elite by fitting a single-peak function on GA search surface. The roughest approximation of a finite searching surface that has one global optimum would be a single-peak curved surface, and the vertex of the approximated single-peak function is expected to be near the global optimum of the original searching surface. We propose two data selection methods for the fitting, use a quadratic function as the single-peak function, and evaluate the proposed idea using seven benchmark functions. The experimental results have shown that the proposed method accelerates GA convergence.
| Original language | English |
|---|---|
| Pages | III-1415 - III-1420 |
| Publication status | Published - Dec 1 1999 |
| Event | Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99 - Seoul, South Korea Duration: Aug 22 1999 → Aug 25 1999 |
Other
| Other | Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99 |
|---|---|
| City | Seoul, South Korea |
| Period | 8/22/99 → 8/25/99 |
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All Science Journal Classification (ASJC) codes
- Software
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics
Cite this
Accelerating a GA convergence by fitting a single-peak function. / Ingu, Takeo; Takagi, Hideyuki.
1999. III-1415 - III-1420 Paper presented at Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99, Seoul, South Korea, .Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Accelerating a GA convergence by fitting a single-peak function
AU - Ingu, Takeo
AU - Takagi, Hideyuki
PY - 1999/12/1
Y1 - 1999/12/1
N2 - This paper proposes an acceleration method of GA search that finds a new elite by fitting a single-peak function on GA search surface. The roughest approximation of a finite searching surface that has one global optimum would be a single-peak curved surface, and the vertex of the approximated single-peak function is expected to be near the global optimum of the original searching surface. We propose two data selection methods for the fitting, use a quadratic function as the single-peak function, and evaluate the proposed idea using seven benchmark functions. The experimental results have shown that the proposed method accelerates GA convergence.
AB - This paper proposes an acceleration method of GA search that finds a new elite by fitting a single-peak function on GA search surface. The roughest approximation of a finite searching surface that has one global optimum would be a single-peak curved surface, and the vertex of the approximated single-peak function is expected to be near the global optimum of the original searching surface. We propose two data selection methods for the fitting, use a quadratic function as the single-peak function, and evaluate the proposed idea using seven benchmark functions. The experimental results have shown that the proposed method accelerates GA convergence.
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SP - III-1415 - III-1420
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