Human-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 where an evolutionary algorithm is hybridized with a local search algorithm to solve both a classic benchmark problem, and a real-world problem.