A Memetic Algorithm Using a Trust-Region Derivative-Free Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Black-box Functions

Tenne, Yoel; Armfield, S.W.

doi:10.1007/978-3-540-49774-5_17

Cited by 14 publications

(9 citation statements)

References 38 publications

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“…-Stability: In the context of dynamic optimization, an adaptive algorithm is called stable if changes in the environment do not affect the optimization accuracy Average best function value (ABFV) (Abbass et al 2004;Eberhart and Shi 2001;Montemanni et al 2003;Schönemann 2004Schönemann , 2007 Average error (Bui et al 2005a, b;Kramer and Gallagher 2003) Current best (Aydin and Ö ztemel 2000;Bosman 2005Bosman , 2007Dam et al 2007;Eriksson and Olsson 2002;Esquivel and Coello Coello 2004;Hanshar and Ombuki-Berman 2007;Laredo et al 2008;Mattfeld and Bierwirth 2004;Michalewicz et al 2007;Neri and Mäkinen 2007;Olivetti de França et al 2005;Shi and Eberhart 2001;Tenne and Armfield 2007;Tumer and Agogino 2007;Venayagamoorthy 2004) Current best evolution (Dam et al 2007;Eberhart and Shi 2001;Fernandes et al 2007;Jin and Sendhoff 2004;Mori et al 2000a, b;Riolo 2005, 2006;Stanhope and Daida 1999;Tinos and Yang 1823) Current best-of-generation evolution (Morrison 2003(Morrison , 2004Quintão et al 2007;Richter 2005;Saleem and Reynolds 2000;Schönemann 2007;Simões and Costa 2003;Tinós and Yang 2007a, b;Wineberg and Oppacher 2000;…”

Section: Measures and Metrics For Assessing The Resultsmentioning

confidence: 98%

Optimization in dynamic environments: a survey on problems, methods and measures

2010

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This paper provides a survey of the research done on optimization in dynamic environments over the past decade. We show an analysis of the most commonly used problems, methods and measures together with the newer approaches and trends, as well as their interrelations and common ideas. The survey is supported by a public web repository, located at http://www.dynamic-optimization. org where the collected bibliography is manually organized and tagged according to different categories.

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Section: Measures and Metrics For Assessing The Resultsmentioning

confidence: 98%

Optimization in dynamic environments: a survey on problems, methods and measures

2010

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“…An inaccurate model can be improved by carefully incorporating x m and possibly a new site x n into the data set [11,12]. The approach can also be used for model selection [13,14].…”

Section: Methods Requiring Additional Sitesmentioning

confidence: 99%

Metamodel accuracy assessment in evolutionary optimization

Tenne

Armfield

2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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Evolutionary optimization of expensive functions typically uses a metamodel, i.e. a computationally cheaper but inaccurate approximation of the objective function. The success of the optimization search depends on the accuracy of the metamodel hence an integral part of the metamodelling framework is assessing the metamodel accuracy. In this paper we survey a range of accuracy assessment methods such as methods requiring additional sites, hypothesis testing and minimum lossfunction methods. We describe two numerical experiments: the first benchmarks different accuracy assessment methods from which it follows the most accurate methods are LOOCV and the 0.632 bootstrap estimator followed by the 10-CV and lastly the holdout method. The second experiment studies the effect of two different accuracy assessment methods on the performance of a typical metamodel-assisted EA, from which it follows the accuracy assessment method has significant effect on the obtained optimum and hence should be chosen corresponding to the objective function features and dimension. We also discuss several issues related to the performance of accuracy assessment methods in practice.

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“…Derivative-free nonlinear optimizers have been applied in the context of black-box optimization (Santarelli and Pellegrino, 2005;Calvel and Mongeau, 2007;Tenne and Armfield, 2007) or simulation optimization (Bengu and Haddock, 1986;Safizadeh and Signorile, 1994;Barton and Ivey, 1996;Bhatnagar and Kowshik, 2005;Kao and Chen, 2006;Horng and Lin, 2009). For problems dealing with discrete variables, classical optimization methods, such as branch and bound, have seen limited application (Čiegis and Baravykaite, 2008) mainly because most of the literature has focused on the application of metaheuristics.…”

Section: Introductionmentioning

confidence: 99%

The challenge of optimizing expensive black boxes: a scatter search/rough set theory approach

Laguna

Molina

Franco

et al. 2010

Journal of the Operational Research Society

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There is renewed interest in the development of effective and efficient methods for optimizing models of which the optimizer has no structural knowledge. This is what in the literature is referred to as optimization of black boxes. In particular, we address the challenge of optimizing expensive black boxes, that is, those that require a significant computational effort to be evaluated. We describe the use of rough set theory within a scatter search framework, with the goal of identifying high-quality solutions with a limited number of objective function evaluations. The rough set strategies that we developed take advantage of the information provided by the best and diverse solutions found during the search, in order to define areas of the solution space that are promising for search intensification. We test our procedure on a set of 92 nonlinear multimodal functions of varied complexity and size and compare the results with a state-of-the-art procedure based on particle swarm optimization.

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A Memetic Algorithm Using a Trust-Region Derivative-Free Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Black-box Functions

Cited by 14 publications

References 38 publications

Optimization in dynamic environments: a survey on problems, methods and measures

Optimization in dynamic environments: a survey on problems, methods and measures

Metamodel accuracy assessment in evolutionary optimization

The challenge of optimizing expensive black boxes: a scatter search/rough set theory approach

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