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ADVANTAGES OF EVOLUTIONARY COMPUTATION USED FOR EXPLORATION IN THE CREATIVE DESIGN PROCESS

机译:创意设计过程中用于探索的进化计算的优势

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In early phases of design a wide exploration of the design space is crucial to the development of creative solutions. In this regard, Evolutionary Computation (EC), and in particular Genetic Algorithms, contain several qualities that can enhance exploration by opening the search process beyond the focus of finding a single "best" solution. Over the years many researchers in the area of creative thinking including Gordon, de Bono, Parnes, Osborn and others, have suggested design strategies that have interesting parallels in EC processes. For instance, a well known inhibitor of creative thinking is design fixation, where the suggestion of a particular solution makes it difficult to imagine other good solutions. Unlike many other computational search algorithms, EC methods work with populations of "fairly good" solutions. Therefore, there is less danger that creativity will be harmed by design fixation on one "best" solution. This paper shows through a specific example of a truss bridge how an EC based design exploration program can aid the designer by providing a selection of "pretty good" solutions rather than a single optimal solution. Other aspects of the EC program are also discussed including drawbacks to the method such as computational intensity as well as directions of future development.
机译:在设计的早期阶段,对设计空间的广泛探索对于开发创意解决方案至关重要。在这方面,进化计算(EC),尤其是遗传算法,包含了几种质量,这些质量可以通过打开搜索过程来扩大探索范围,而不再只是寻找单个“最佳”解决方案。多年来,包括Gordon,de Bono,Parnes,Osborn等在内的许多创新思维领域的研究人员提出了与EC流程相似的设计策略。例如,众所周知,创造性思维的阻碍因素是设计固定,在这种情况下,特定解决方案的建议使人们难以想象其他好的解决方案。与许多其他计算搜索算法不同,EC方法适用于“相当好的”解决方案。因此,很少有风险将设计固定在一个“最佳”解决方案上而损害创造力。本文通过一个桁架桥的特定示例,说明了基于EC的设计探索程序如何通过提供“非常好的”解决方案而非单个最佳解决方案的选择来帮助设计人员。还讨论了EC程序的其他方面,包括该方法的缺点,例如计算强度以及未来的发展方向。

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