Classical and Evolutionary Algorithms in the Optimization of Optical Systems

✍ Scribed by Darko Vasiljević (auth.)

• Publisher: Springer US
• Year: 2002
• Tongue: English
• Leaves: 281
• Edition: 1
• Category: Library

✦ Synopsis

The optimization of optical systems is a very old problem. As soon as lens designers discovered the possibility of designing optical systems, 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 systems. In recent years modern optimization methods have been developed that are not primarily based on the known mathematical theories of optimization, but rather on analogies with nature. While searching for successful optimization methods, scientists noticed that the method of organic evolution (well-known Darwinian theory of evolution) represented an optimal strategy of adaptation of living organisms to their changing environment. If the method of organic evolution was very successful in nature, the principles of the biological evolution could be applied to the problem of optimization of complex technical systems.

✦ Table of Contents

Front Matter....Pages i-xiii
Introduction....Pages 1-9
Classical algorithms in the optimization of optical systems....Pages 11-39
Genetic Algorithms....Pages 41-67
Evolution Strategies....Pages 69-82
Comparison of optimization algorithms....Pages 83-88
Ray trace....Pages 89-99
Aberrations....Pages 101-117
Damped least squares optimization implementation....Pages 119-136
Adaptive steady-state genetic algorithm implementation....Pages 137-154
Two membered evolution strategy ES EVOL implementation....Pages 155-164
Multimembered evolution strategies ES GRUP and ES REKO implementation....Pages 165-174
Multimembered evolution strategy ES KORR implementation....Pages 175-186
The Cooke triplet optimizations....Pages 187-211
The Petzval objective optimizations....Pages 213-236
The Double Gauss objective optimizations....Pages 237-260
Summary and outlook....Pages 261-263
Back Matter....Pages 265-279

✦ Subjects

Artificial Intelligence (incl. Robotics)