Summary
This chapter introduces individual evolution as a strategy for problem solving. This strategy proposes to partition the original problem into a set of homogeneous elements, whose individual contribution to the problem solution can be evaluated separately. A population comprised of these homogeneous elements is evolved with the goal of creating a single solution by a process of aggregation. The goal of individual evolution is to locally build better individuals that jointly form better global solutions. The implementation of the proposed approach requires addressing aspects such as problem decomposition and representation, local and global fitness integration, as well as diversity preservation mechanisms. The benefit of applying the individual evolution approach for problem solving is a substantial reduction in computational effort expended in the evolutionary optimization process. This chapter shows an example from vision metrology where experimental results coincide with previous state of the art photogrammetric network design methodologies, while incurring in only a fraction of the computational cost.
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Olague, G., Dunn, E., Lutton, E. (2008). Individual Evolution as an Adaptive Strategy for Photogrammetric Network Design. In: Cotta, C., Sevaux, M., Sörensen, K. (eds) Adaptive and Multilevel Metaheuristics. Studies in Computational Intelligence, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79438-7_8
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DOI: https://doi.org/10.1007/978-3-540-79438-7_8
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