企業價值評估是評估企業的合理價值，有效的價值評估機制可提昇企業經營績效進而創造投資人的財富。傳統財務建構的評估模式受限於許多的假設前題，以及依特定順序組合線性評估模型，至今仍存在一些爭議問題。此外，相同的評估模型所計算的評估值在不同的證券市場可能產生不同的研究結果，此意謂著企業合理價值可能是多值 (multi-valued) 而非單值 (single-valued)。本研究的目的是提出「模糊遺傳程式規劃」，作為建構評估模型新技術，期望能有系統性與自動化建構新的多值企業價值評估模型。研究方法採用遺傳程式規劃(Genetic Programming, GP) 技術為基礎，將染色體模糊化協助發掘企業合理價值，藉選擇 (selection)、交配 (crossover)、突變 (mutation) 與評估 (evaluation) 機制不斷演化出新族群，最後搜尋出最佳的規則組合，做為評估企業合理價值的準則。智慧型多值企業價值評估模型，是以遺傳程式規劃為基礎，加入模糊數 (fuzzy number) 的編碼，傳統的交配方法並不全然適用。本研究提出模糊樹 (fuzzy tree) 混合交配法修正子樹交配法，加入模糊數節點的交配，期望提昇演化效能。此外， 梯形模糊數適合多值的企業價值評估，並導引出自然的投資組合之資產配置交易策略，色括買入、賣出與合理股價策略，依隸屬度高低決定資金配置部位。實證結果發現：一、以模糊樹混合交配法所需的執行時間較子樹交配法短，投資績效也較好。二、多值企業價值評估的平均報酬率均較單值與買入持有佳。

Business valuation plays an important role in stock selection for fundamental investors. However, it may be difficult to use stock valuation results because different models generate different estimates on the same stock. This may imply that the value of a stock should be multi-valued rather than single-valued. In this study, we develop an intelligent business valuation model to produce a multi-valued price for a stock by generalizing genetic programming to fuzzy genetic programming. This study generalizes the subtree crossover to design a new crossover operator for the fuzzy trees. Since the stock value is estimated by a fuzzy expression tree which calculates to a fuzzy number, the stock value becomes multi-valued. In addition, the resulting trapezoidal fuzzy stock value induces naturally a trading strategy which can readily be executed and evaluated. Experimental results indicate that the Fuzzy Tree crossover is more effective than subtree crossover in terms of expression tree complexity and run time. Finally, the return of multi-valued fuzzy trading strategy is better than that of single-valued and Buy-and-Hold strategy. We suggest that more attention should be put on the multi-valued business valuation approach.