Abstract
Interactive evolutionary computation is a technique that performs optimization based on human evaluation, and we have proposed an image retrieval method based on the emotion using interactive genetic algorithm. This approach allows to search images not only with explicitly expressed keyword but also abstract keyword such as 'cheerful impression image' and 'gloomy impression image'. It searches the goal with a small population size and generates fewer number of generations than that of conventional genetic algorithm to reduce user's burden. But this property may derive local minimum and sometimes more poor solution than random search method owing to relatively small size population. In order to solve this problem, we suggest an idea of sparse fitness evaluation method using clustering method and fitness allocation method. This aims to allow not only to keep the advantages of interactive GA but also to improve the performance by utilizing large population.
| Original language | English |
|---|---|
| Publication status | Published - 1999 Dec 1 |
| Event | Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99 - Seoul, South Korea Duration: 1999 Aug 22 → 1999 Aug 25 |
Other
| Other | Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99 |
|---|---|
| City | Seoul, South Korea |
| Period | 99/8/22 → 99/8/25 |
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All Science Journal Classification (ASJC) codes
- Software
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics
Cite this
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Sparse fitness evaluation for reducing user burden in interactive genetic algorithm. / Lee, Joo Young; Cho, Sung Bae.
1999. 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 - Sparse fitness evaluation for reducing user burden in interactive genetic algorithm
AU - Lee, Joo Young
AU - Cho, Sung Bae
PY - 1999/12/1
Y1 - 1999/12/1
N2 - Interactive evolutionary computation is a technique that performs optimization based on human evaluation, and we have proposed an image retrieval method based on the emotion using interactive genetic algorithm. This approach allows to search images not only with explicitly expressed keyword but also abstract keyword such as 'cheerful impression image' and 'gloomy impression image'. It searches the goal with a small population size and generates fewer number of generations than that of conventional genetic algorithm to reduce user's burden. But this property may derive local minimum and sometimes more poor solution than random search method owing to relatively small size population. In order to solve this problem, we suggest an idea of sparse fitness evaluation method using clustering method and fitness allocation method. This aims to allow not only to keep the advantages of interactive GA but also to improve the performance by utilizing large population.
AB - Interactive evolutionary computation is a technique that performs optimization based on human evaluation, and we have proposed an image retrieval method based on the emotion using interactive genetic algorithm. This approach allows to search images not only with explicitly expressed keyword but also abstract keyword such as 'cheerful impression image' and 'gloomy impression image'. It searches the goal with a small population size and generates fewer number of generations than that of conventional genetic algorithm to reduce user's burden. But this property may derive local minimum and sometimes more poor solution than random search method owing to relatively small size population. In order to solve this problem, we suggest an idea of sparse fitness evaluation method using clustering method and fitness allocation method. This aims to allow not only to keep the advantages of interactive GA but also to improve the performance by utilizing large population.
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M3 - Paper
ER -