建立在一般結(jié)構(gòu)Gauss網(wǎng)絡(luò)上的分布估計算法
Estimation of Distribution Algorithm Based on Generic Gaussian Networks
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摘要: 提出了一種建立在一般結(jié)構(gòu)Gauss網(wǎng)絡(luò)上的分布估計算法。一方面，它無需進行Gauss網(wǎng)絡(luò)結(jié)構(gòu)的學習，從而大大減少了計算量，另一方面，一般結(jié)構(gòu)Gauss網(wǎng)絡(luò)不是近似網(wǎng)絡(luò)，因而可獲得精度很高的聯(lián)合概率密度函數(shù)。針對該網(wǎng)絡(luò)，采用了一種無需計算條件概率密度函數(shù)的產(chǎn)生樣本方法，有效地減少了網(wǎng)絡(luò)參數(shù)學習的計算開銷。實驗結(jié)果表明，與已有建立在非一般結(jié)構(gòu)Gauss網(wǎng)絡(luò)上的高階分布估計算法相比，本文算法具有更高的穩(wěn)定性和更強的尋優(yōu)能力。
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關(guān)鍵詞:
- 進化計算; 分布估計算法; Gauss網(wǎng)絡(luò)
Abstract: Estimation of Distribution Algorithms (EDAs) available in continuous domains are based on non-generic Gaussian networks. The computational cost for learning this kind of networks is very great, moreover the low accuracy of Ihe joint pdf will be resulted because the greedy algorithm is used to learn the Gaussian networks. To overcome these disadvantages, an Estimation of Distribution Algorithm based on generic Gaussian Networks (GN-EDA) is presented. Ft leads to the low computational cost by no structure learning of Gaussian networks. In the meanwhile, a generic Gaussian network is not an approximate one, so the joint pdf is of high accuracy. Due to an effective sampling is adopted, the computational cost for parameters learning is great reduced. The experimental results show that GN-EDA achieves a more stable performance and a stronger ability in searching the global optima. -
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