Interactive genetic algorithms for shape preference assessment in engineering design
Author: Kelly, Jarod C.
Supervisor: Papalambros, Panos Y.
Institution: Mechanical Engineering, The University of Michigan
In the design of artifacts it is important to realize that designs are judged according to functional as well as subjective measures. This is especially true in markets where the technology behind the particular artifact is well established, and the costs of production are uniform across the market. In such cases, users are faced with a decision: the selection of one item from amongst a broad range of similar offerings. The shape of an object, its geometric features, can be one element of the artifact that may elevate it above the competition in user choice. The preference that users have for artifact shape is not a scientifically understood field. Such studies and pursuits are generally the province of artists and industrial designers, who are quite adept at applying their intuition and skills to assess markets and design products to fit them. However, the shape of an object can also have an important impact on its performance. If this is the case, then engineers must be involved in analyzing the design to ensure that it meets performance and safety criteria. This intersection between the form and the function of an artifact lacks tools and applied methods that can allow designers and producers to make scientifically informed decisions to understand the impact of shape preference on performance and vice versa. This dissertation explores how current preference tools can be applied to the understanding of shape preference, as it relates to a specific artifact. It is shown here that a meaningful quantification of both shape preference and performance can be obtained and used in decision making. It is also shown here that interactive genetic algorithms are a tool capable of understanding shape preference. Further, the capacity for interactive genetic algorithms to enhance creativity is also shown.