線上拍賣已經越來越盛行且廣泛地被大家所接受，因此有愈來愈多的人從事線上拍賣的機制設計，以促進線上拍賣的安全性與效率性。在線上拍賣中，我們會發現同樣的拍賣品重複的被拍賣，我們稱此種拍賣為重複拍賣，而重複拍賣中學習效果是一個非常重要的現象，另外，在動態的競標過程當中，超額出標卻是一個奇特的現象，這些現象是受到許多的因素所影響而造成的。在本研究中，吾人將探討三個參數，競標者的人數，重複拍賣的次數以及物品的市場價格作為控制變數，而其影響的結果平均報酬，超額出標的頻率和超額出標的比例。吾人利用人工代理人的機制，以基因演算法來模擬現實生活中的交易情況，加以觀察及分析拍賣的結果。吾人發現幾個有趣的現象：(1)當競標者數目增加的時候，競標者平均報酬會減少，而超額出標的頻率會增加，超額出標的比例會減少; (2)當物品市場價格增加的時候，競標者平均報酬會增加，而超額出標的頻率和比例都會減少; (3)當重複進行拍賣的次數增加的時候，競標者的平均報酬不固定，但是超額出標的頻率和比例都會增加。由於基因演算法裡面有擇優、交配和突變，讓競標者的策略更多樣化與學習效果的更全面化，因此讓吾人能在重複的線上拍賣中觀察到這些有趣的現象。

On-line auction becomes more and more popular now. Many people engage in developing the mechanism that makes on-line auction safety and effective. The learning effect is an important impression in the repeated on-line auction. The phenomenon over-bidding will be brought by the dynamic evaluation from the learning effect in the repeated on-line auction. There are many factors influencing the dynamic evaluation of the on-line auction. In this research, three parameters, number of bidders, number of blocks and market price of product are investigated to influence the results including average payoff over price, over-bidding frequency and over-bidding rate. We use artificial agent instead of the participators in the auction and simulation analysis with genetic algorithm to observe and analyze the results. We find some interesting trends in the repeated on-line auction. (1) When the number of bidders increases, the average payoff / price decreases, the over-bidding frequency increases, and the over-bidding rate decreases. It is due to the more number of bidders, the more competitive they are. (2) When the market price of product increases, the average payoff / price increases, the over-bidding frequency decreases, and the over-bidding rate decreases. It is due to facing of higher price product, bidders will be more cautious to bid. (3) When the number of blocks increases, the average payoff / price is uncertain, the over-bidding frequency increases, and the over-bidding rate increases. It is due to the learning effect of the bidders and the mutation of genetic algorithm. Thus it can be seen, the learning effect affect the auction result indeed.

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