Summary
This chapter centres around the use of interactive evolutionary computation as a search and exploration tool for open-ended contexts in design. Such contexts are characterized by poor initial definition and uncertainty in terms of objectives, constraints and defining variable parameters. The objective of the research presented is the realization of ‘user-centric’ intelligent systems, i.e., systems which can overcome initial lack of understanding and associated uncertainty, whilst also stimulating innovation and creativity through a high degree of human / machine interaction. Two application areas are used to illustrate how, through the adoption of bespoke visualization techniques, flexible representations, and machine learning agents that ‘observe’ the evolutionary process, this objective can be achieved.
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Parmee, I.C., Abraham, J.A.R., Machwe, A. (2008). User-Centric Evolutionary Computing: Melding Human and Machine Capability to Satisfy Multiple Criteria. In: Knowles, J., Corne, D., Deb, K. (eds) Multiobjective Problem Solving from Nature. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72964-8_13
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