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consists of three parts: (1) a candidate solution generator, (2) the candidate solution acceptance function, and (3) the nondominated-solutions archive The candidate solution generator is an (1 + 1)-ES, where the individual that survives to the next generation is based on dominance If the parent dominates the o spring, the latter is rejected, and the parent survives If the o spring dominates the parent, the o spring survives to the next generation If neither the parent nor the o spring is dominating, the o spring is compared with the nondominated solutions in the archive, as summarized in Algorithm 124 The archive maintains a set of nondominated solutions to the MOO The size of the archive is restricted When an o spring dominates the current solutions in the archive, it is included in the archive When the o spring is dominated by any of the solutions in the archive, the o spring is not included in the archive When the o spring and the solutions in the archive are nondominating, the o spring is accepted and included in the archive based on the degree of crowding in the corresponding area of objective space To keep track of crowding, a grid is de ned over objective space, and for each cell of the grid a counter is maintained to keep track of the number of nondominated solutions for that part of the Pareto front When an o spring is accepted into the archive, and the archive has reached its capacity, the o spring replaces one of the solutions in the highest populated grid cell (provided that the grid cell corresponding to the o spring has a lower frequency count) When the parent and its o spring are nondominating, the one with the lower frequency count is accepted in the archive The PAES is summarized in Algorithm 123 Costa and Oliveira [159] developed a di erent approach to ensure that nondominated solutions survive to next generations, and to produce diverse solutions with respect to objective space Fitnesses of individuals are based on a Pareto ranking, where individuals are grouped into a number of Pareto fronts An individual s tness is determined based on the Pareto front in which the individual resides At each generation, the Pareto ranking process proceeds as follows All of the nondominated solutions from the o spring, or + parents and o spring, form the rst Pareto front These individuals are then removed, and the nondominating solutions from the remainder of the individuals form the second Pareto front This process of forming Pareto fronts continues until all (or + ) individuals are assigned to a Pareto front Individuals of the rst Pareto front is assigned a tness of 1/nc , where nc is the niche count The niche count is the number of individuals in this front that lies within a distance of share from the individual (distance is measured with respect to objective space) The threshold, share , is referred to as the niche radius Individuals of the next Pareto front is assigned a tness of (1 + fworst )/nc , where fworst is the worst tness from the previous front This process of tness assignment continues until all individuals have been assigned a tness The tness sharing approach described above promotes diversity of nondominated solutions The process of creating Pareto fronts ensures that dominated individuals are excluded from future generations as follows: Depending on the ES used, individuals. hiqpdf azure Azure HTML to PDF Converter Library for .NET, ASP.NET, MVC and ...
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asp.net pdf viewer component 20 Jul 2015 ... Noxum GmbH, which develops content management and editing systems, announced its PDF as a Service is now available in the Microsoft ... vb.net add image to pdf Algorithm 123 Pareto Archived Evolution Strategy Set generation counter; Generate a random parent solution; Evaluate the parent; Add the parent to the archive; while stopping condition(s) not true do Create o spring through mutation; Evaluate o spring; if offspring is not dominated by parent then Compare o spring with solutions in the archive; Update the archive using Algorithm 124; if offspring dominated parent then O spring survives to new generation to become the parent; end else //nondominating case Select the individual that maps to the grid cell with lowest frequency count; end end end Algorithm 124 Archive Update Algorithm used by PAES Input: A candidate solution and an archive of nondominated solutions; Output: An updated archive; if candidate is dominated by any member of the archive then Reject the candidate; end else if candidate dominates any solutions in the archive then Remove all dominated members from the archive; Add candidate to the archive; end else if archive is full then if candidate will increase diversity in the archive then Remove the solution with the highest frequency from the archive; end Add candidate to the archive; end end end azure read pdf Leverage OCR to full text search your images within Azure Search ...
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