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Genetic algorithms in computer-aided design
journal contributionposted on 24.06.2015 by Ian Graham, Keith Case, Robert Wood
Any type of content formally published in an academic journal, usually following a peer-review process.
This paper describes progress in research into the development of a computer-aided design (CAD) tool that aids designers in generating the form of a product by the use of evolutionary techniques. Genetic algorithm (GA) software has been developed and combined with a commercial CAD solid modelling system. The system creates objects that initially have the appearance of being random in form, but which can be subjected to a user-directed selective breeding programme which is also guided by pre-set internal, or environmental, factors. User scoring of each object, or an objective function, determines which objects are considered to be the ‘fittest’, and thus likely to become parents of the next generation. Through the co-operation of the user and the pre-set environmental factors, the forms on the screen progressively become more than an abstract collection of geometric primitives. It is believed that this can provide useful inspiration with regard to the aesthetics and functional characteristics of products, and the potential exists for this approach to be the basis of a new design methodology. Early work demonstrated that the software had the ability to evolve interesting shapes in line with a user’s particular criteria for rating objects. However, it was also obvious that the objects generated needed enhancement to convincingly represent some of the geometric complexities of real products. Using the CAD software’s blend function within the evolutionary process has provided that complexity, producing excellent results and greatly widening the field of application. In addition to simply creating secondary geometry through the smoothening of sharp edges or creation of curved fillets between adjoining solids, more significant and complex primary geometric forms have been generated by allowing relatively large blend radii. The current challenge is to combine the existing ability to predictably evolve simple geometric shapes, with the added complexity arising from the use of blends, to make the concept genuinely useful. The outcome of genetic manipulation needs to be predictable, to the extent that desirable features from objects are reproduced in subsequent generations. The key to this is the way the genetic shape defining data is stored and processed, especially the way the blend instructions are integrated into the existing genetic structure, and this is the major focus of continuing research.
- Mechanical, Electrical and Manufacturing Engineering