Abstract
Cluster Oriented Genetic Algorithms (COGAs) have the ability to identify high-performance (HP) regions of complex design spaces through the on-line filtering of solutions generated by a genetic algorithm [1]. COGAs support the designer by providing relevant information relating to the characteristics of HP regions. This can lead to the identification of best design direction during early stages of design or to reduce the complexity of design space through a reduction in variable range or the conversion of problem variables to fixed parameters both during conceptual and detailed design. The paper introduces the initial variable mutation cluster oriented genetic algorithm (vmCOGA) before briefly describing more recent improvements and their implications. Examples then follow of the utilisation of COGAs both for exploratory conceptual design and for variable space reduction during more rigorous stages of the design process.
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Parmee, I.C. (2004). A Review of the Development and Application of Cluster Oriented Genetic Algorithms. In: Burczyński, T., Osyczka, A. (eds) IUTAM Symposium on Evolutionary Methods in Mechanics. Solid Mechanics and Its Applications, vol 117. Springer, Dordrecht. https://doi.org/10.1007/1-4020-2267-0_31
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DOI: https://doi.org/10.1007/1-4020-2267-0_31
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