Genetic Programming for large tree optimization with considering inheritance and acquisition of traits
| Project/Area Number |
26330290
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Soft computing
|
|---|
| Research Institution | Kansai University |
|---|
Principal Investigator |
Hanada Yoshiko 関西大学, システム理工学部, 准教授 (30511711)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
小野 景子 龍谷大学, 理工学部, 講師 (80550235)
|
|---|
| Project Period (FY) |
2014-04-01 – 2018-03-31
|
| Project Status |
Completed (Fiscal Year 2017)
|
| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
|---|
| Keywords | 遺伝的プログラミング / 木構造 / 局所探索 / 初期化 / 交叉 / 確率モデル / 近傍探索 |
|---|
| Outline of Final Research Achievements |
The purpose of this study is to develop a new genetic programming for large tree optimization in order to solve the design problem consisting of variables expressed in tree structure such as expressions and program generation for autonomous control. The achieved works of this study are: (1) Improvement of a multi step crossover that has high trait integrity, (2) Development of an initialization scheme based on preferable trait estimation by past search information, and (3) Development of a search scheme for obtaining new traits. We showed the effectiveness and application guidelines of the proposed method mainly through the performance analysis using the function regression problem.
|
Report
(5 results)
Research Products
(21 results)
