April 2025
Intermediate
240 pages
6h 56m
Japanese
Content preview from AI開発力を鍛える！機械学習と最適化による問題解決講座
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図 3.3.2 パレートフロントの形状からの考察
本 項 の まとめ
多目的最適化では、トレードオフの関係にある複数の目的関数に対して、パ
レート解・パレートフロントを得ることが、解を得ることに相当します。パレー
ト解・パレートフロントを得ることには次の恩恵があります。
• パレート解・パレートフロントが得られると、トレードオフの限界点・妥協点
がわかる。
• パレートフロントの形状からも、複数のグループの存在など重要な情報を得る
ことができる。
多目的最適化の次元の呪い
前項で見たように、多目的最適化におけるパレートフロントは、目的関数の数
を
とすると、
-1次元となります。マイナス1されるとはいえ、目的関数の数
のべき乗でパレートフロントの空間が広がりますので、パレートフロントを求め
るためのパレート解の個数もべき乗で増加します。そのため、これまでに見た、
3.3.2
2.
3.
0.
1.
175
3.3
機械学習の入力パラメータ数に対する必要な教師データ数（2.2節）、最適化の入
力パラメータ数に対する探索空間の広さ（3.1節）と同様に、次元の呪いの問題
が存在します。例えば、遺伝的アルゴリズムの ...
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