In this paper, we proposed a new approach called “evolutionary genetic algorithm” that improves the efficiency and the quality of the simple genetic algorithm (GA) in constructing parallel tests. The basic principle of this evolutionary genetic algorithm combines two theories. One is that of genetic diversity, which is beneficial to species evolutionary existence. The other is eugenic theory, which can increase the probability of finding better offspring. Experimental results show that our approach is much better than the simple genetic algorithm in terms of time efficiency and solution quality. The evolutionary genetic algorithm would be a more powerful tool than the simple genetic algorithm for parallel test construction. 本研究將提出一新的演算方法”演化式基因演算法(evolutionary genetic algorithm)”應用於平行測驗建構上。此演算方法主要結合兩個概念：即生物多樣性(genetic diversity) 以及優生學理論(eugenic theor)。自然界中，生物多樣性有利於物種演化綿延不絕；優生學概念則強調產生更佳的下一子代。結合這兩個概念於本研究所提出之演化式演算方法，其實驗結果顯示，此演算方法比起傳統基因演算法具有更高的效率以及得到更佳的解。同時也證明此演化式基因演算法將成為平行測驗建構更有效的工具。
