The Lens Design Using the CMA-ES Algorithm
نویسنده
چکیده
This paper presents a lens system design algorithm using the covariance matrix adaptation evolution strategy (CMA-ES), which is one of the most powerful self-adaptation mechanisms. The lens design problem is a very difficult optimization problem because the typical search space is a complicated multidimensional space including many local optima, non-linearities, and strongly correlated parameters. There have been several applications of Evolution Algorithms (EA) to lens system designs, and Genetic Algorithms (GAs) are generally expected to be more suitable for these kind of difficult optimization problems than Evolution Strategy(ES) because GAs can provide a global optimization. We demonstrate, however, that a CMA-ES can work better than the GA methods previously applied to lens design problems. Experimental results show that the proposed method can find human-competitive lens systems efficiently.
منابع مشابه
Task Scheduling Algorithm Using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in Cloud Computing
The cloud computing is considered as a computational model which provides the uses requests with resources upon any demand and needs.The need for planning the scheduling of the user's jobs has emerged as an important challenge in the field of cloud computing. It is mainly due to several reasons, including ever-increasing advancements of information technology and an increase of applications and...
متن کاملHuman-competitive lens system design with evolution strategies
Lens system design provides ideal problems for evolutionary algorithms: a complex nonlinear optimization task, often with intricate physical constraints, for which there is no analytical solutions. This paper demonstrates, through the use of two evolution strategies, namely non-isotropic SA-ES and CMA-ES, as well as multiobjective NSGA-II optimization, the human competitiveness of an approach w...
متن کاملTHE CMA EVOLUTION STRATEGY BASED SIZE OPTIMIZATION OF TRUSS STRUCTURES
Evolution Strategies (ES) are a class of Evolutionary Algorithms based on Gaussian mutation and deterministic selection. Gaussian mutation captures pair-wise dependencies between the variables through a covariance matrix. Covariance Matrix Adaptation (CMA) is a method to update this covariance matrix. In this paper, the CMA-ES, which has found many applications in solving continuous optimizatio...
متن کاملThe Multi-objective Variable Metric Evolution Strategy, Part I
The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most powerful evolutionary algorithms for real-valued single-objective optimization. Here a variant of the CMA-ES for multi-objective optimization (MOO) is developed. First a single-objective, elitist CMA-ES using plus-selection and step size control based on a success rule is introduced. This algorithm is compared to th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2004