In evolutionary computation many different representations ("genomes") have been suggested as the underlying data structures, upon which the genetic operators act. Among the most prominent examples are the evolution of binary strings, real-valued vectors, permutations, finite automata, and parse trees. In this paper the use of place-transition nets, a low-level Petri net (PN) class [1,2], as the structures that undergo evolution is examined. We call this approach "Petri Net Evolution" (PNE). Structurally, Petri nets can be considered as specialized bipartite graphs. In their extended version (adding inhibitor arcs) PNs are as powerful as Turing machines. PNE is therefore a form of Genetic Programming (GP). Preliminary results obtained by evolving variablesize place-transition nets show the success of this approach when applied to the problem areas of boolean function learning and classification.
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机译：在进化计算中，许多不同的表示（“基因组”）被建议为基本数据结构，遗传运营商的作用。在最突出的例子中是二进制字符串，实值载体，排列，有限自动机和解析树的演变。在本文中，使用地方过渡网，低水平的Petri网（PN）类[1,2]，作为经过演化的结构。我们称之为“Petri Net Evolution”（PNE）。在结构上，Petri网可以被认为是专门的双链图。在其扩展版本（添加禁止弧线）中，PNS与图灵机一样强大。因此，PNE是一种遗传编程（GP）的形式。通过演化变量化的地位过渡网获得的初步结果显示了这种方法在应用于布尔函数学习和分类的问题领域时的成功。
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