多目標最佳化最困難的地方，莫過於如何在多個目標之間取捨，以取得最佳的平衡點。投資人在選股時，也面臨這樣的兩難。因為在選擇投資組合的過程中，需要滿足報酬率和風險等績效目標，由於報酬率和風險兩者間相互衝突的特性，使得投資人往往不清楚其偏好或各目標之相對重要性為何，因此在評估最佳投資組合的過程中，會同時產生多組效率投資組合，此相當於多目標最佳化過程中，所產生的柏拉圖最佳解。故投資組合選擇問題是一複雜的多目標最佳化問題。
　　為達到最佳化多項評估指標的目的，過去諸多研究，常常將這些投資組合評估指標，以加權方式結合成單一目標函數，這樣的方法並不能解決目標之間互相衝突的情況，雖然有釵h其他傳統最佳化方法可解決此問題，然而，這些傳統方法的最大限制在於，一次只能求得一個最佳解，且求解的過程相當繁瑣。
　　本研究提出此多目標最佳化選股策略架構的目的，為幫助投資人解決運用公司基本面資料選擇最佳投資組合時，必須在多個衝突的評估函數間取捨的困境；同時使用多目標遺傳演算法技術實作此多目標最佳化選股系統，以解決傳統最佳化方法所遇到的瓶頸與限制。經由台灣股市實證結果顯示，在實驗期間，多目標最佳選股策略的所有目標績效大致上比類股、加權指數佳；相較於單目標最佳選股策略，多目標最佳選股策略的目標績效則大致上不比前者差，或甚至更好，代表此多目標最佳選股策略是能滿足投資人的所有目標績效之非超越解。由此驗證本研究架構的確能運用於解決多目標最佳化的選股問題上。

Real-world problems involve multiple objectives that need to be optimized simulta-neously. So does investment problem. In this paper, we propose a framework of stock portfo-lio selection strategies based on MOGA and using fundamental data of company’s financial statements. MOGA is well suited to solve these multi-objective optimization problems since a family of “acceptable” solutions – a Pareto set – can be identified by different individuals through the evaluation process. We implement the framework with VEGA, a kind of MOGA method. Our preliminary experiments show that the results of these MOGA-based strategies are promising.

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