Genetic programming and self-organization

提出的研究计划的目标是研究进化计算领域的一种特定方法，即遗传规划（GP）与自组织领域之间的关系。这种研究的必要性来自于传统形式的GP已经达到了发展的平台期。这项技术目前正被应用于许多科学和工程领域，但它的局限性日益明显。GP的一个关键障碍是可扩展性问题，即难以使用当前的程序表示来发展大型程序。另一个困难是与人工进化的主要目标相互作用的各种非预期的、突现的进化现象。我们的目的是研究GP和自组织之间的关系，通过引入各种创新的方案表示（以及相应的算子产生新的方案变体）。有些将是非顺序的（就像我们最近对随机序列的研究），有些将是自我进化的表示，有些将是复杂的。一种很有前景的表示是由具有调控元件（如自然界中的调控网络）的基因组组成的。其他申述将增加发展的内容。这里提出的研究将有助于计算机科学的一个充满活力的研究领域，包括进化计算。在应用方面，生物信息学和计算金融领域存在许多适合利用遗传规划的问题，这对加拿大有实质性的好处。

The goal of the proposed research program is to study the relation between a particular method in the area of Evolutionary Computation, namely Genetic Programming (GP) , and the field of Self-Organization. The need for such a study comes from the realization that GP in its traditional form has reached a plateau of development. The technique is presently being exploited in many fields of Science and Engineering, yet its limitations become clearer every day. One key obstacle for GP is the problem of scalability, i.e. the difficulty to evolve large programs using present-day representations of programs. Another difficulty is the interaction of various non-intended, emergent phenomena of evolutionary runs with the primary goal of the artificial evolution.We aim at studying the relationship between GP and self-organization by introducing various innovative representations of programs (together with corresponding operators producing new variants of programs). Some will be non-sequential (like in our recent work on random sequences), some will be self-evolving representations, some will be otherwise complex. A representation with considerable promise is one consisting of a genome with regulatory elements (like regulatory networks in nature). Other representations will have elements of development added.The research proposed here will contribute to a vibrant research field of Computer Science, including Evolutionary Computation. In applications, the areas of Bioinformatics  and Computational Finance hold many problems that are suitable for exploitation with Genetic Programming, with substantial benefits to Canada.