By Darko Vasiljevic
The optimization of optical structures is a truly outdated challenge. once lens designers came upon the opportunity of designing optical platforms, the need to enhance these platforms by way of the technique of optimization all started. for a very long time the optimization of optical structures was once hooked up with famous mathematical theories of optimization which gave stable effects, yet required lens designers to have a robust wisdom approximately optimized optical structures. lately glossy optimization tools were constructed that aren't based at the identified mathematical theories of optimization, yet quite on analogies with nature. whereas looking for winning optimization tools, scientists spotted that the tactic of natural evolution (well-known Darwinian idea of evolution) represented an optimum technique of variation of dwelling organisms to their altering setting. If the strategy of natural evolution used to be very profitable in nature, the foundations of the organic evolution will be utilized to the matter of optimization of complicated technical systems.
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Extra info for Classical and Evolutionary Algorithms in the Optimization of Optical Systems
The simple procedure just described is a basis for most variations of genetic algorithms. In the next sections the following parts of genetic algorithms will be analysed: the various ways for the representation of individuals; the different methods for the selection of individuals for the reproduction; the numerous genetic operators. 2 Representation of individuals in the genetic algorithm The way in which individuals are represented is a central factor in the success of a genetic algorithm. Most GA applications use fixed length, fixed order bit strings to encode individuals.
Each individual is assigned a merit function value according to how good a solution to the problem is. The highly fit individuals are given chance to reproduce by cross breeding with other highly fit parents. The least fit members of the population are less likely to get selected for reproduction and so die out. A whole new population of possible solutions is thus produced by selecting the best individuals from the current generation and mating them to produce a new set of individuals. This new generation contains a higher proportion of the characteristics possessed by the good members of the previous generation.
The sigma scaling was designed as an improvement of linear scaling. It deals with the negative merit function values and incorporates the problem dependent information into the scaling function. e. the degree to which highly fit individuals are allowed to have many offspring) relatively constant over the course of the optimization rather than depending on the merit function variances in the population. Under the sigma scaling, an individual's scaled value for the merit function is a function of: its merit function; the mean and the standard deviation of all merit functions of the population in the current generation.
Classical and Evolutionary Algorithms in the Optimization of Optical Systems by Darko Vasiljevic