This paper presents new lens optimization methods based on real-coded genet
ic algorithms (GAs). We take advantage of GA's capability of global optimiz
ation and multi-objective optimization against two serious problems in conv
entional lens optimization techniques: (1) choosing a starting point by tri
al and error, and (2) combining multiple criteria to a single criterion. In
this paper, two criteria for lenses, the resolution and the distortion, ar
e considered. First, we propose a real-coded GA that optimizes a single cri
terion, a weighted sum of the resolution and the distortion. To overcome a
problem of the difficulty in generating feasible lenses especially in large
-scale problems, we introduce a feasibility enforcement operator to modify
an infeasible solution into a feasible one. By applying the proposed method
to some small-scale problems, we show that the proposed method can find em
pirically optimal and suboptimal lenses. We also apply the proposed method
to some relatively large-scale problems and show that the proposed method c
an effectively work under large-scale problems. Next, regarding the lens de
sign problem as a multi-objective optimization problem, we propose a real-c
oded multi-objective GA that explicitly optimizes the two criteria, the res
olution and the distortion. We show the effectiveness of the proposed metho
d in multi-objective lens optimization by applying it to a three-element le
ns design problem. (C) 2000 Published by Elsevier Science S.A. All rights r
eserved.