As it can be seen from the historical overview in Chapter 1, the development of optical design methods is firmly connected to the development of computers. Today a single most influential factor in the development of optical design procedures is availability of inexpensive and powerful personal computers. First optical designers were satisfied only to design an optical system because the computer speed was barely enough for this task. As soon as the computer speed was increased the desire for the optimization of optical system was born. First there were classical optimization methods optimization methods based on famous mathematical (numerical) methods. These optimization methods were very good and solved many problems. But one problem remained with these optimization methods that cannot be solved. These optimization methods requested a good knowledge of the optical system being optimized from the optical designer. For the classical optical systems like objectives this was not a great difficulty because they were very well researched. But for new types of optical systems or very specific optical systems this was a great problem because of no prior knowledge available. The knowledge of the optical system was used for selecting good starting points because the optimization methods were able to find only the local minimum closest to the starting point. There is no guarantee that the found local minimum is in fact the global minimum. The optical designer usually has to start optimization from several different starting points and to compare the optimization results. With the introduction of modern optimization methods like the genetic algorithms and the evolution strategies the problem of selecting good starting points is diminished. The evolutionary algorithms (the genetic algorithms and the evolution strategies) work with the population of randomly chosen starting systems so they have a possibility to escape from the local minimums that are not optimum solutions. Today very powerful optimization methods both classical and evolutionary are present and they allow design and optimization of almost any optical system.


Genetic Algorithm Local Minimum Evolutionary Algorithm Optical System Optical Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Darko Vasiljević
    • 1
  1. 1.Military Technical Institute, Yugoslavia Military Academy, Yugoslavia Mechanical Engineering FacultyUniversity of BelgradeYugoslavia

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