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医療情報システム研究室
データマイニング班
【文献調査】
Multi-stage genetic programming: A new strategy to
nonlinear system modeling
白石 駿英
廣安 知之
山本 詩子
2014 年 09 月 30 日
タイトル
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マルチ段階遺伝的プログラミング: 非線形系モデリングへの新しい戦略
著者
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Amir Hossein Gandomi, Amir Hossein Alavi
出典
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Information Sciences, Volume 181, Issue 23, 1 December 2011, Pages 5227-5239
アブストラクト
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この論文は非線形系のモデル化のために新しいマルチ段階遺伝的プログラミング (MSGP) 戦略を示す．提案さ
れた戦略は，より正確なシミュレーションを提供するために説明変数の個々の影響およびそれらの中の相互作用を
組込むことに基づいている．MSGP 戦略によれば，問題のための効率的な公式化は異なるタームを含んでいると
考えられる．MSGP ベースの分析の初期では，出力変数は影響を及ぼす変数から公式化されます．その後，実際
のものの間のエラーおよび予測値は新しい変数から公式化される．最後に相互作用タームは，個々に高度に発展し
たタームまでに予言された実際の値と値の間の差の公式化により引き出されます．MSGP の能力は，異なる複雑
なエンジニアリング問題の公式化にそれを適用することにより説明される．ここに分析された問題は下記を含ん
でいる: (i)pH 中和過程のシミュレーション，(ii) 終了製粉業での表面の粗さの予測および (iii) 土壌液状化条件の
分類である．提案された戦略の有効性は，分析に含まれていなかった実験結果の部分に派生模型を適用することに
より確認される．さらに，モデルの外部確認は他の研究者に勧められたいくつかの統計的規準を使用して確認され
る．MSGP ベースの解決策は，調査されたシステムの非線形の振る舞いを有効にシミュレートすることができる．
MSGP のその結果は，標準 GP および人工ニューラルネットワーク・ベースのモデルのものより正確となった．
キーワード
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Multi-stage genetic programming, Nonlinear system modeling, Engineering problems, Formulation
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6.1
参考文献
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6.2
遺伝的プログラミングを用いた機械学習について
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6.3
ニューラルネットワークについて
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6.4
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