Genetic Programming Techniques for Modelling and Design

计算智能是人工智能的一个分支，它使用受自然界现象启发的解决问题的算法。我的研究方向是遗传编程（GP）。这是一种受达尔文进化论启发的计算智能技术，用于自动“繁殖”计算机程序来解决各种问题。我对使用GP解决进化设计中的问题很感兴趣。这是一个研究领域，涉及不同领域的复杂设计问题，如3D建模、建筑、艺术、生物网络等。设计领域的一个共同主题是假设解决方案本质上需要高度的人类智慧和创造力。此外，与GP中研究的其他问题不同，对于给定的设计问题，通常可能有许多不同的解决方案。例如，有许多有用的节能建筑设计。******我的研究将通过提高GP在进化设计中的有效性来解决日益复杂的设计问题，并将重点关注进化设计中的几个主要技术问题。首先，复杂的设计问题通常涉及大量针对具体问题的考虑，如果要获得满意的解决方案，就需要解决所有这些问题。其中一些因素的计算也可能非常昂贵，例如，使用能源模拟软件估计建筑物的能源使用情况。我将探索在解决现实问题时有效处理许多必要设计目标的方法。我还将使用显卡中的高性能计算能力来帮助加快这些昂贵计算的性能。这将使处理能力大大提高，从而使进化设计系统更加高效和强大。计算机速度的提高将允许创建软件支持工具，允许用户与GP交互工作以找到合适的设计。第二个研究方向将涉及创造新的设计语言用于遗传编程。这些语言将描述针对特定问题的许多特定于领域的设计目标，并提高GP解决问题的能力。通过解决这两个问题，为复杂的、现实世界的设计问题自动创建解决方案将成为可能。在研究过程中，我将在艺术应用的2D图形，3D建模，节能建筑和生物建模中探索新的设计问题。GP的这些改进也将广泛适用于进化设计之外的问题领域。长期目标是利用这项技术为最终用户创建更智能的支持软件

Computational intelligence is a branch of artificial intelligence that uses problem-solving algorithms inspired by phenomena seen in nature. My research is in genetic programming (GP). This is a computational intelligence technique inspired by Darwin's theory of evolution, which is used to automatically "breed" computer programs to solve a variety of problems. I am interested in the use of GP to solve problems in evolutionary design. This is a research area involving complex design problems found in diverse areas such as 3D modelling, architecture, art, biological networks, and others. A common theme in design fields is the assumption that solutions inherently require a high degree of human ingenuity and creativity. Furthermore, unlike other problems studied in GP, many different solutions are often possible for a given design problem. For example, there are a multitude of useful designs for an energy-efficient building.******My research will solve increasingly complex design problems by improving the effectiveness of GP in evolutionary design, and will focus on a few major technical issues in evolutionary design. First, complex design problems usually involve a large number of problem-specific considerations, all of which need to be solved if satisfactory solutions are to be obtained. Some of these factors can be very expensive to compute as well, for example, estimating the energy usage of a building using energy simulation software. I will explore ways to effectively handle the many design goals necessary when solving real-world problems. I will also be using the high-performance computing power found in graphics cards, to help speed up the performance of these expensive computations. This will enable enormous increases in processing power, which will make the evolutionary design system much more efficient and powerful. This improvement in computer speed will permit the creation of software support tools that allow users to work interactively with the GP to find suitable designs. A second direction of research will involve creating new design languages to be used by genetic programming. These languages will describe many of the domain-specific design goals for specific problems, and improve the problem-solving abilities of GP. By tackling these two issues, the automatic creation of solutions to complex, real-world design problems will be possible. During the course of the research, I will explore new design problems throughout 2D graphics for art applications, 3D modelling, energy efficient architecture, and biological modelling. These improvements to GP will also be widely applicable to problem areas outside of evolutionary design. A long-term goal is to use this technology to create more intelligent support software for end-users.**