The Lens Design Using the CMA-ES Algorithm

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.

• Type: Conference
• Year: 2004
• Where: GECCO
• Authors: Yuichi Nagata
• Keywords: Difficult Optimization Problems, GECCO 2004, Lens Design Problems, Matrix Adaptation Evolution