Genetic programming and self-organization

该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）和自组织领域之间的关系。需要这样的研究来自于认识到，GP在其传统形式已经达到了发展的高原。该技术目前正在科学和工程的许多领域中使用，但其局限性每天都变得更加清晰。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进化大型程序。另一个困难是各种非预期的，新兴的现象的相互作用的进化运行的主要目标的人工evolution.We旨在研究GP和自组织之间的关系，通过引入各种创新的表示程序（连同相应的运营商产生新的变种程序）。有些将是非序列的（如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。具有相当大前景的表示是由具有调控元件的基因组组成的表示（类似于自然界中的调控网络）。其他表示将有发展的元素添加。这里提出的研究将有助于一个充满活力的计算机科学研究领域，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问题，这些问题适合利用遗传编程，对加拿大有很大的好处。

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.