A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm

Optimum design of a grazing-incidence flat-field spectrograph with a spherical varied-line-space grating

A grazing-incidence spectrograph is designed by use of the flat-field image-focusing property of a spherical varied-line-space grating. Optimum grating parameters for mechanical ruling are selected by application of genetic algorithms. Two gratings, one for 2-5-nm and the other for 5-20-nm spectral regions, are designed, and their fabrication tolerances are analyzed.

Optimization of road networks using evolutionary strategies

A road network usually has to fulfill two requirements: (i) it should as far as possible provide direct connections between nodes to avoid large detours; and (ii) the costs for road construction and maintenance, which are assumed proportional to the total length of the roads, should be low. The optimal solution is a compromise between these contradictory demands, which in our model can be weighted by a parameter. The road optimization problem belongs to the class of frustrated optimization problems. In this paper, a special class of evolutionary strategies, such as the Boltzmann and Darwin and mixed strategies, are applied to find differently optimized solutions (graphs of varying density) for the road network, depending on the degree of frustration. We show that the optimization process occurs on two different time scales. In the asymptotic limit, a fixed relation between the mean connection distance (detour) and the total length (costs) of the network exists that defines a range of possible compromises. Furthermore, we investigate the density of states, which describes the number of solutions with a certain fitness value in the stationary regime. We find that the network problem belongs to a class of optimization problems in which more effort in optimization certainly yields better solutions. An analytical approximation for the relation between effort and improvement is derived.