Adaptive Representations for Genetic Algorithms and Local Search

遗传算法和局部搜索的自适应表示这是一项为期三年的持续奖励的第一年资助。PI最近的理论结果证明，当用作搜索和参数优化问题的表示时，与标准二进制编码相比，反射格雷码具有优势（例如，可以使用一种简单的机制，通过在不同的反射格雷码之间自适应切换来逃避局部最优）。初步的实证结果表明，使用高精度的自适应Gray码可以获得更好的优化效果，并证明了新的收敛结果。在这个项目中，将开发一组高精度自适应格雷码，用于遗传算法和局部搜索。PI将进一步对进化算法、局部搜索和其他不需要衍生信息的启发式搜索方法进行广泛的比较评估。一个现实世界的应用将涉及到新一代更准确的天气预报系统。CloudSat项目将在未来几年内部署一个基于卫星的云结构建模系统，该项目开发的搜索方法将是解决此类模型核心关键逆问题的最佳和最快方法，因此，它们似乎很可能被列入2003年CloudSat计划的部署中。

Adaptive Representations for Genetic Algorithms and Local SearchThis is the first year funding of a three year continuing award. The PI's recent theoretical results prove there are advantages to reflected Gray codes compared to standard binary encodings when used as a representation for search and parameter optimization problems (e.g., it is possible to use a simple mechanism to escape local optima by adaptively switching between different reflected Gray codes). Preliminary empirical results indicate that using high precision adaptive Gray codes leads to better optimization, and new convergence results have been proven. In this project, a family of high precision adaptive Gray codes will be developed for use with genetic algorithms and local search. The PI will further conduct a broad comparative evaluation of evolutionary algorithms, local search and other heuristic search methods that do not require derivative information. A real-world application will be addressed related to a new generation of much more accurate weather prediction systems. The CloudSat project will in the next few years deploy a satellite-based system for modeling cloud structures, and the search methods developed in this project will be the best and fastest way to solve a key inverse problem which lies at the heart of such models, as a consequence of which they appear to be likely candidates for inclusion in the scheduled 2003 deployment of CloudSat.