在投資組合中，每一不同的資金配置量和不同的投資標的物之選擇即構成不同之投資組合。但在現實投資問題中，可供選擇的投資標的物非常眾多，若同時評估多種投資標的物與多種資金配置比例的選取，則所構成的投資組合數將極為龐大。因此，並不適合以窮舉法的方式進行求解，必需求助諸如「遺傳演算法」等人工智慧的柔性運算方式來輔助搜尋出滿意解。
　　而過去利用遺傳演算法在解決投資組合問題時，常會因編碼方式的限制，而產生不當擴大搜尋空間或造成不同染色體間資金比重的表達缺乏一致性，以及浪費大量計算資源在處理非法解或正規化上等問題。
　　但若透過本研究所提供之「組合編碼式遺傳演算法」，即可有效使編碼表達空間與實際問題的解答空間吻合，因此可以增加搜尋速度並增進搜尋效果。經由本研究之資金分配實驗，在民國八十七年至八十九年期間，在證券市場的實際測試結果，顯示透過組合編碼方式可以補充傳統遺傳演算法的編碼方式的不足，在解決組合問題的應用時，可以有效避免正規化運算和非法解的排除等資源浪費。更可以快速且有效的找出較佳之投資組合，且不論在空頭市場或多頭市場中均可有較佳之報酬率。

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