本文關鍵詞：基于無監(jiān)督特征學習的演化計算行為分析　出處：《中國科學技術大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 演化計算 行為分析 無監(jiān)督學習 自組織映射 慢特征分析 深度信念網絡 受限玻爾茲曼機

【摘要】：演化計算作為一類啟發(fā)式優(yōu)化方法,其在解決真實世界中的復雜優(yōu)化問題時的良好性能已經在過去的幾十年中得到了很好的驗證。但是演化計算自身復雜的隨機行為導致對其進行理論分析異常困難,時至今日,仍然難以找到一種有效的方法來對演化算法在不同環(huán)境下的行為進行學習和分析。為了更好地理解演化計算的行為,本文嘗試采用無監(jiān)督特征學習的方法,對演化計算在搜索過程中的一代群體行為進行分析。首先對所研究的演化計算行為數據進行定義,然后從基于自組織映射的演化計算行為數據預處理、基于慢特征分析的演化計算行為數據特征提取和基于深度信念網絡的演化計算行為數據特征提取三個方面入手,對演化計算的行為數據進行了特征提取和分析。具體工作如下:1)研究了基于自組織映射的演化計算行為數據預處理方法。研究了基于t分布隨機鄰域嵌入(t-SNE)的自組織映射網絡預訓練方法,從而將自組織映射網絡的訓練分為二個階段:預訓練、粗訓練和微調三個階段,使得網絡能夠收斂到最好的狀態(tài)。然后使用訓練好的自組織映射神經網絡將原始高維空間中的演化計算行為數據映射到二維平面上,實現高維數據集的歸一化表示,為后續(xù)使用無監(jiān)督特征提取算法對演化計算行為數據進行分析做好數據準備。2)研究了基于慢特征分析算法的演化計算行為數據特征提取算法。首先對慢特征分析算法應用到無監(jiān)督模式識別問題時的時間序列結構調整進行了研究,同時對需要保留的慢特征維數也進行了分析和計算。然后針對演化計算行為數據的特點,設計了基于二階非線性擴展慢特征分析算法的特征提取框架,對演化計算行為數據進行特征提取。最后設計了多組對比實驗,分別研究了不同演化算法在同樣的landscape下的行為特征差異,以及同一演化算法在不同的landscape下的行為特征差異。實驗結果表明,慢特征分析算法可以提取到不同演化算法之間具有判別性的穩(wěn)定特征。3)研究了基于深度信念網絡的演化計算行為特征提取算法。首先對深度信念網絡的基本組成單元——受限玻爾茲曼機,進行了詳細研究。然后針對要分析的演化計算行為數據,設計了一個包含有七層受限玻爾茲曼機網絡的深度信念網絡框架。最后設計實驗得到了不同演化算法在同一個測試函數下的行為數據經過深度信念網絡提取到的特征分布結果,將該結果與慢特征分析提取到的特征進行對比,對選用的四種演化算法的行為進行了分析。
[Abstract]:Evolutionary computing is a kind of heuristic optimization method. Its good performance in solving complex optimization problems in the real world has been well verified in the past several ten years. However, the complex stochastic behavior of evolutionary computation makes it extremely difficult to theoretically analyze it. . Up to now, it is still difficult to find an effective way to study and analyze the behavior of evolutionary algorithms in different environments, in order to better understand the behavior of evolutionary computing. This paper attempts to use the unsupervised feature learning method to analyze the behavior of the generation of evolutionary computing in the search process. Firstly, the data of evolutionary computing behavior are defined. Then we preprocess the evolutionary behavior data based on self-organizing mapping. There are three aspects: feature extraction of evolutionary computing behavior data based on slow feature analysis and feature extraction of evolutionary computing behavior data based on deep belief network. The behavior data of evolutionary computing are extracted and analyzed. The main work is as follows: 1) the preprocessing method of evolutionary computing behavior data based on self-organizing mapping is studied, and the random neighborhood embedding based on t-distribution is studied. T-SNE-based self-organizing mapping network pretraining method. Thus, the self-organizing mapping network training is divided into two stages: pre-training, rough training and fine-tuning. The network can converge to the best state, and then use the trained self-organizing mapping neural network to map the evolutionary computing behavior data in the original high-dimensional space to the two-dimensional plane. The normalized representation of high-dimensional data sets is realized. Prepare the data for the analysis of evolutionary computing behavior data using unsupervised feature extraction algorithm. 2). The feature extraction algorithm of evolutionary computing behavior data based on slow feature analysis algorithm is studied. Firstly, the time series structure adjustment of slow feature analysis algorithm is studied when it is applied to unsupervised pattern recognition problem. At the same time, the dimension of slow feature which needs to be preserved is also analyzed and calculated. Then, a feature extraction framework based on second-order nonlinear extended slow feature analysis algorithm is designed according to the characteristics of evolutionary computing behavior data. Finally, we design a number of comparative experiments to study the behavior characteristics of different evolutionary algorithms under the same landscape. And the behavior characteristics of the same evolutionary algorithm under different landscape are different. The experimental results show that. Slow feature analysis algorithm can extract stable features with discriminant property between different evolutionary algorithms. 3). An evolutionary behavior feature extraction algorithm based on deep belief network is studied. Firstly, the constrained Boltzmann machine, which is the basic component of the deep belief network, is studied. Then the evolutionary behavior data to be analyzed are studied in detail. In this paper, we design a framework of deep belief network with seven layers of constrained Boltzmann machine network. Finally, we design experiments to obtain the behavior data of different evolutionary algorithms under the same test function, which are extracted by the deep belief network. Characteristic distribution results. The results are compared with the features extracted by slow feature analysis, and the behavior of the four evolutionary algorithms is analyzed.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP181

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