演化策? (evolution strategies) 通常應用於目標變?為實?值的最佳化問題上，其主要是?用突變 (mutation) 的方式於解答空間中搜尋出最好的滿足解，?用?似的演化過程有許多組合最佳化的問題合適且成功地被解決，如二次指派問題 (quadratic assignment problems)，工程設計最佳化問題 (engineering design optimization problems) 等等，但一般的演化計算技術應用於這些問題時，將會?費很多的時間於處?在演化過程中獲得無效解的情形上，以致於造成演算法效??彰。本研究提出一種新的染色體編碼方式，稱為?徑編碼法 (the path-encoding method)，其修改動態規劃的?徑搜尋概?於組合演化策? (combinatorial evolution strategies) 上，以提高其演化效能，並以 NP-hard 的生產分派問題 (production allocation problems) ?當成測試的實?，在實驗中對?徑編碼法、組合編碼法 (combination-encoding method)、懲罰編碼法 (penalty-encoding method) 和整?規劃法 (integer programming) 等四種方法做一比較，結果顯示出本研究所提之?徑編碼法效果最好，其次是組合編碼法，再者是懲罰編碼法，最後是整?規劃法，其主要的原因是?徑編碼法可以有效地縮小搜尋解答空間的範圍，以加快收斂的速?。

Evolution strategies are applied to optimize real-valued vectors of objective variables. These strategies rely primarily on mutation to explore the solution search space. Many combinatorial optimization problems such as quadratic assignment problems, engineering design optimization problems, and others can be successfully solved by analog evolution. This evolutionary algorithm wastes much time in managing invalid solutions and is typically less efficient.This paper presents a new approach, called the path-encoding method, which modifies the path searching idea of dynamic programming for combinatorial evolution strategies to enhance the performance of evolutionary process. The NP-hard production allocation problem is used to evaluate the effectiveness of the approach. This experiment compares the proposed approach to the combination-encoding method, the penalty-encoding method and integer programming. The computed results show that the proposed approach is always feasible and outperforms the others because it narrows the solution search space.