Alexandre Mayer (UNamur)

Title: An evolutionary computing approach to optical engineering

Abstract: We present a genetic algorithm that we developed in order to address computationally expensive optimization problems in optical engineering. The idea consists in working with a population of individuals that represent possible solutions to the problem. The best individuals are selected. They generate new individuals for the next generation. Random mutations in the coding of parameters are introduced. This strategy is repeated from generation to generation until the algorithm converges to the global optimum of the problem considered. For computationally expensive problems, it makes sense to analyze the data collected by the algorithm in order to infer more rapidly the final solution. The objective is ideally to determine the global optimum by a single run of the genetic algorithm and with a reduced number of fitness evaluations. The use of a mutation operator that acts on randomly-shifted Gray codes contributes to this objective by helping the genetic algorithm to escape local optima and by enabling a wider diversity of displacements. These techniques reduce the computational cost of optical engineering problems, where the design parameters have a finite resolution and are limited to a realistic range.