Human-competitive lens system design with evolution strategies

Gagné, Christian; Beaulieu, Julie; Parizeau, Marc; Thibault, Simon

doi:10.1016/j.asoc.2007.10.018

Cited by 31 publications

(15 citation statements)

References 29 publications

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“…The results could be used to improve the GA for optimization problems in real life lens design[45].Gagné et al applied EAs to an optimization criteria with complex mechanical constraints. Their results showed that EAs are comparable to those obtained by humans[46].…”

mentioning

confidence: 52%

“…al. [46] discovered that on a real-life imaging problem the EA lens showed better results by a factor of almost two and was four times more sensitive than the expert lens design for the 1990 monochromatic quartet [40]. Moreover, an EA can explore a lens system, which is similar to a design performed by experts.…”

Section: First Studies Of Ga In Lens Designmentioning

confidence: 97%

“…• Lens design and optimization method with GA applied to liquid lens elements Fang et al [52] • Hybrid GA to optimize a 350X zoom optics system Tsai et al [55] • ES applied to a human-competitive lens system design Gagné et al [46] • Application of evolutionary algorithms to lens design Thibault et al [1] 2009 • Hybrid Taguchi-GA applied to zoom optics including a DOE Fang et al [54] 2011 • An altered GA applied to optical design and optimization of the digital zoom optics with liquid lenses Fang et al [35] 2013…”

Section: Reading Light Systemmentioning

confidence: 99%

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Genetic algorithms for lens design: a review

Höschel

Lakshminarayanan

2018

J Opt

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Genetic algorithms (GAs) have a long history of over four decades. GAs are adaptive heuristic search algorithms that provide solutions for optimization and search problems. The GA derives expression from the biological terminology of natural selection, crossover, and mutation. In fact, GAs simulate the processes of natural evolution. Due to their unique simplicity, GAs are applied to the search space to find optimal solutions to various problems in science and engineering.Using GAs for lens design was investigated mostly in the 1990s, but were not fully exploited. But in the past few years there have been a number of newer studies exploring the application of GAs or hybrid GAs in optical design. In this paper we discuss the basic ideas behind GAs and demonstrate their application in optical lens design.

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mentioning

confidence: 52%

Section: First Studies Of Ga In Lens Designmentioning

confidence: 97%

Section: Reading Light Systemmentioning

confidence: 99%

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Genetic algorithms for lens design: a review

Höschel

Lakshminarayanan

2018

J Opt

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“…The reason we choose CMA-ES here is that CMA-ES has been shown effective for difficult non-linear non-convex black-box optimization problems in continuous domains with anywhere from 3 to 100 variables. CMA-ES has yielded competitive results for local and global optimization [18,17,2] and is now a well-established method with many different application domains [12,27,19].…”

Section: Introductionmentioning

confidence: 99%

Evolution Strategies for Exploring Protein Energy Landscapes

Clausen

Sapin

Jong

et al. 2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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The focus on important diseases of our time has prompted many experimental labs to resolve and deposit functional structures of disease-causing or disease-participating proteins. At this point, many functional structures of wildtype and disease-involved variants of a protein exist in structural databases. The objective for computational approaches is to employ such information to discover features of the underlying energy landscape on which functional structures reside. Important questions about which subset of structures are most thermodynamically-stable remain unanswered. The challenge is how to transform an essentially discrete problem into one where continuous optimization is suitable and effective. In this paper, we present such a transformation, which allows adapting and applying evolution strategies to explore an underlying continuous variable space and locate the global optimum of a multimodal fitness landscape. The paper presents results on wildtype and mutant sequences of proteins implicated in human disorders, such as cancer and Amyotrophic lateral sclerosis. More generally, the paper offers a methodology for transforming a discrete problem into a continuous optimization one as a way to possibly address outstanding discrete problems in the evolutionary computation community.

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“…These disciplines are characterized by nonlinear, multiobjective dynamical systems that face major obstacles of getting stuck in nonoptimal solutions and premature convergence [20][21][22]. Metaheuristics is a class of powerful stochastic algorithms, which have been proved over the years as efficient and fast problem solvers [23][24][25]. Computational intelligence (CI) belongs to this class of metaheuristic search techniques [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Support vector machine firefly algorithm based optimization of lens system

et al. 2014

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Lens system design is an important factor in image quality. The main aspect of the lens system design methodology is the optimization procedure. Since optimization is a complex, nonlinear task, soft computing optimization algorithms can be used. There are many tools that can be employed to measure optical performance, but the spot diagram is the most useful. The spot diagram gives an indication of the image of a point object. In this paper, the spot size radius is considered an optimization criterion. Intelligent soft computing scheme support vector machines (SVMs) coupled with the firefly algorithm (FFA) are implemented. The performance of the proposed estimators is confirmed with the simulation results. The result of the proposed SVM-FFA model has been compared with support vector regression (SVR), artificial neural networks, and generic programming methods. The results show that the SVM-FFA model performs more accurately than the other methodologies. Therefore, SVM-FFA can be used as an efficient soft computing technique in the optimization of lens system designs.

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Human-competitive lens system design with evolution strategies

Cited by 31 publications

References 29 publications

Genetic algorithms for lens design: a review

Genetic algorithms for lens design: a review

Evolution Strategies for Exploring Protein Energy Landscapes

Support vector machine firefly algorithm based optimization of lens system

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