PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Towards a model of exploration in computer-aided design
Proceedings of the IFIP TC5/WG5.2 Workshop on Formal Design Methods for CAD
An open computing infrastructure that facilitates integrated product and process development from a decision-based perspective
Genetically Engineered Architecture - Design Exploration with Evolutionary Computation
Genetically Engineered Architecture - Design Exploration with Evolutionary Computation
Multimodal size, shape, and topology optimisation of truss structures using the Firefly algorithm
Advances in Engineering Software
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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.