使用卷積神經網路作影像分類時，卷積核對圖像特徵擷取，有著舉足輕重之
影響，本文提出利用遺傳演算法結合卷積神經網路，搜尋對於不同的影像資料，
具有最佳特徵擷取能力的卷積核，以進行影像分類。

本文實驗首先藉由遺傳演算法，產生不同形狀與大小的卷積核個體後，利用
適應函數結合卷積神經網路，用以評估各個體之卷積核對於影像資料的分類準確
率，再以適合度進行卷積核篩選，持續進行交配、突變演化，達成擇優演化最佳
卷積核之目的，並自動儲存到目前為止，所搜尋到的最佳卷積核模型，直到滿足
停止的條件時結束。

在實驗影像資料庫中，本文方法以遺傳演算法結合卷積神經網路，搜尋最佳
卷積核作影像分類，搜尋到之最佳卷積核，確實會優於傳統人工依憑經驗，所挑
選的正方形卷積核；據實驗結果顯示，卷積核的形狀與面積，皆會造成卷積神經
網路辨識之準確率差異；同時從實驗結果中發現，卷積神經網路與遺傳演算法結
合後，可以對不同影像資料庫，搜尋到具有較佳特徵擷取能力的卷積核，提升卷
積神經網路影像辨識之準確率；本文使用遺傳演算法結合卷積神經網路之方法，
對於較為複雜的影像資料或龐大數據資料，在優化卷積核的效能可更為明確，用
以達到節省人工調整參數的時間，並有效提升卷積神經網路之辨識準確率。

Filters of convolutional neural network have important impact on extracting features for image classification. This paper proposes a method which combines convolutional neural network (CNN) and genetic algorithm(GA), so as to search the optimized filter in various image databases for image classification.

This paper utilizes GA to generate individuals which contain filters of different shapes and sizes. Then GA combines fitness function with CNN together, in order to evaluate the fitness of each individual’s performance for image classification. According to the fitness, GA selects the individuals for the next generation, and it can achieve the purpose of optimizing filters. In the process, GA also picks the best model with optimized filters to store. The evolution process will stop, if the stop condition is satisfied.

In the experiment image databases, this paper uses GA to be combined with CNN to search for the optimized filter for image classification. Human usually rely on experience to adjust the filters, whereas the filters searched by this paper are certainly better than the square filters adjusted by human in most experiments. Outcomes show that the shapes and the sizes of the filters indeed cause the difference of accuracy for image classification. The results, such as combining GA and CNN for image classification, show that it can find the filters with stronger capability to extract features for different image databases, and increase the accuracy for image classification. Using GA and CNN can be more effective to optimize the filters, if the images are more complicated and enormous. Furthermore, it also can decrease the cost and time compared with human effort.

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