Titlebook: Classical and Evolutionary Algorithms in the Optimization of Optical Systems; Darko Vasiljevi? Book 2002 Kluwer Academic Publishers 2002 e

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, the desire to improve those systems by the means of optimization began. For a long time the optimization of optical systems was connected with well-known mathematical theories of optimization which gave good results, but required lens designers to have a strong knowledge about optimized optical sy

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SPER

https://doi.org/10.1007/978-3-540-49658-8al optimization algorithm, that can solve all problems occurring in practice, does not exist. All the optimization algorithms presently known can only be used without restriction in particular areas of application. According to the nature of a particular problem one or another optimization algorithm offers a more successful solution.

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Sheryl L. Chatfield,Jeffrey S. Hallamly designed for the technical system optimization. It has some differences from the rest of genetic algorithms, which makes the ASSGA a good algorithm for optimization. The theory of the ASSGA along with other genetic algorithms is given in Chapter 3.

Analogy

Jacqueline W. Curtis,Andrew Curtisrm of the Petzval objective has been retained in many present-day applications, but detailed changes have been made in the doublets. In some instances, for example, the doublets have been replaced by doublets plus associated singlets. In other variants of the basic form a third doublet has been added.

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Genetic Algorithms,ittest” first described by Charles Darwin in a famous book . [25]. By mimicking this process, genetic algorithms are able to “develop — evolve” solutions to real world problems. The foundations of genetic algorithms were first laid down rigorously by Holland in [26] and De Jong in [27]. De Jong first applied genetic algorithms in the optimization.

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Comparison of optimization algorithms,al optimization algorithm, that can solve all problems occurring in practice, does not exist. All the optimization algorithms presently known can only be used without restriction in particular areas of application. According to the nature of a particular problem one or another optimization algorithm offers a more successful solution.

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savage

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