A Comparison of Immune and Genetic Algorithms for Two Real-Life Tasks of Pattern Recognition
A Comparison of Immune and Genetic Algorithms for Two Real-Life Tasks of Pattern Recognition
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2005
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遗传算法的领域是完善的今天，它形成了进化计算和计算智能的重要组成部分[1，2]。然而，自然免疫系统的计算能力直到最近才在人工免疫系统（AIS）的新领域中得到重视[3，4]。这些能力的数学形式化[5]形成了IC作为一种新的计算方法的基础，该方法复制了蛋白质和免疫网络的信息处理原理[6]。此外，免疫网络的IC模型[6，7，8]与AIS中使用的IC模型不同。尽管AIS利用了GA的一些思想，从而相互增强（参见，例如，[9，10]），IC和GA之间还没有直接的比较。本文更具体地探讨了这一领域，这已经探讨了以前在一个相当概念的方式。
The field of GAs is well-established nowadays and it forms an important part of evolutionary computation and computational intelligence [1,2]. However, the computing capabilities of the natural immune system have only recently been appreciated within a new field of Artificial Immune Systems (AISs) [3,4]. The mathematical formalization of these capabilities [5] forms the basis of IC as a new computing approach that replicates the principles of information processing by proteins and immune networks [6]. Besides, IC models of immune networks [6,7,8] differ from those utilizing within AIS. Although AISs exploit some ideas of GAs, thus enhancing one another (see, e.g., [9,10]), no direct comparison between IC and GA has yet been available. This paper investigates more specifically this area, which has been explored before in a rather conceptual way.