摘要或動機

此研究之重點在於如何建構一套人工智慧方法，人工智慧含多種進行方式，例如以類神經網路訓練近似於人腦之結構，而專家系統係於不同的科學領域內以自己之所知判斷。我們先以 John Holland 的 Genetic Algorithm （暫譯基因演算法，以下暫稱GA）的理論來實作出一套人工智慧系統之方法。Genetic Programming 係以ＧＡ為基礎之實作方法，主要的內容不出基本的演化定義；在這次的試驗中，首要為先定出程式欲演化成何種類型，在此我們先定義為排序型的演算法，經過分析實際程式之結構後定出適合基因元件的資料結構，分化為兩個部份進行，一為產生器，亦為突變，交配器，一為評審程式，亦為執行器，兩者使用相同之基本元件，再以不斷的交配和突變以達到全域最佳化。我們將兩種部份完成後，加上現有的PVM 分散運算函式庫來增加演算的速度。
Abstract The research mainly discusses AI. AI, involves in several types, for example, neural network(NN),which adopts human-like training method; Expert systems determine and make decision by what it has known. We will use John Koza's Genetic Programming theory to implement an AI system.Genetic Programming theory is based on GA. In our experiments, we have to define what kind of applications we want : a sorting application, which is divided into two parts - a producer (along with crossover and mutation operations) and an executor (along with judging operations), is an easyimplemented algorithm. Our program, also with PVM, will approaches global optimal after evolutions.