Individual evolution as an adaptive strategy for photogrammetric network design

Gustavo Olague, Enrique Dunn, Evelyne Lutton

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationAdaptive and Multilevel Metaheuristics
EditorsCarlos Cotta, Marc Sevaux, Kenneth Sörensen
Pages157-176
Number of pages20
DOIs
StatePublished - 2008

Publication series

NameStudies in Computational Intelligence
Volume136
ISSN (Print)1860-949X

Keywords

  • Coevolution
  • Individual evolution
  • Photogrammetric network design

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