Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Classical and Evolutionary Algorithms in the Optimization of Optical Systems
Classical and Evolutionary Algorithms in the Optimization of Optical Systems
Automated re-invention of six patented optical lens systems using genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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Significant improvement over a patented lens design is achieved using multi-objective evolutionary optimization. A comparison of the results obtained from NSGA2 and 茂戮驴-MOEA is done. In our current study, 茂戮驴-MOEA converged to essentially the same Pareto-optimal solutions as the one with NSGA2, but 茂戮驴-MOEA proved to be better in providing reasonably good solutions, comparable to the patented design, with lower number of lens evaluations. 茂戮驴-MOEA is shown to be computationally more efficient and practical than NSGA2 to obtain the required initial insight into the objective function trade-offs while optimizing large and complex optical systems.