Gene Expression Programming - a new machine learning technique for supervised and unsupervised classification

许多科学、工程和商业领域，如遗传学、医学、环境科学与工程、物理学、天文学、金融和市场营销，都面临着处理复杂数据以提取特定领域知识的共同挑战。为了智能地帮助用户提取这些知识，需要有效的数据分析技术。该项目将利用最近开发的计算机算法基因表达编程的基本思想来解决这一需求，以开发新的进化算法技术和新的有监督和无监督数据分类算法。该项目将开发和利用新的同源遗传算子和机制来控制算法提供的解决方案中的冗余信息，以提高其效率。这些发展将与最先进的统计方法相结合，如促进学习，以创建有效的数据分类算法。项目中开发的方法和算法将在软件应用程序中实施，并作为开源程序提供，以便最大限度地扩大项目成果的受益者范围。

Many scientific, engineering and business fields such as genetics, medicine, environment science and engineering, physics, astronomy, finance, and marketing are facing common challenges in dealing with complex data for extracting field-specific knowledge. Efficient data analysis techniques are needed in order to intelligently assist the user in extracting this knowledge. This project will address this need using the basic ideas of a recently developed computer algorithm, Gene Expression Programming, for the development of novel evolutionary algorithms techniques and novel supervised and unsupervised data classification algorithms. The project will develop and exploit novel homologous genetic operators, and mechanisms to control the redundant information in the solutions provided by the algorithm in order to increase its efficiency. These developments will be combined with state-of-the-art statistical methods such as boosting learning in order to create efficient data classification algorithms.The methods and algorithms developed in the project will be implemented in software applications made available as open-source in order to maximize the spectrum of the beneficiaries of the project outcomes.