| 作成者 |
|
|
|
| 本文言語 |
|
| 出版者 |
|
| 発行日 |
|
| 収録物名 |
|
| 開始ページ |
|
| 終了ページ |
|
| 会議情報 |
|
| 出版タイプ |
|
| アクセス権 |
|
| 権利関係 |
|
| 関連DOI |
|
| 概要 |
We introduce the acceptability of a decision maker to handle evolutionary multi-objective optimization (EMO) on design space, while most of EMO research tries to find many solutions on an objective sp...ace and passes them to a decision maker. Unlike this conventional EMO approaches, our approach decides maker's model with the concept of acceptability and introduces it in EMO search. Especially, this approach works well when qualitative factors, such as the decision maker's experience and knowledge on a task, are a part of evaluations. Acceptability functions for each of objectives are aggregated firstly, and the aggregated acceptability forms contours on an objective space and is mapped on a design space. The acceptability contours on a design space can narrow down the area of solutions. We could find better solutions in our experiments than the conventional approach of searching solutions on an objective space.続きを見る
|
Mendeley出力
IntConf149