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On the Effect of Response Transformations in sequential parameter optimization

EVOLUTIONARY COMPUTATION 2012年第2期20卷 229-248页

作者： Wagner, Tobias Wessing, Simon TU Dortmund Inst Machining Technol ISF D-44227 Dortmund Germany TU Dortmund Chair Algorithm Engn D-44227 Dortmund Germany

parameter tuning of evolutionary algorithms (EAs) is attracting more and more interest. In particular, the sequential parameter optimization (SPO) framework for the model-assisted tuning of stochastic optimizers has resulted in established parameter tuning algorithms. In this paper, we enhance the SPO framework by introducing transformation steps before the response aggregation and before the actual modeling. Based on design-of-experiments techniques, we empirically analyze the effect of integrating different transformations. We show that in particular, a rank transformation of the responses provides significant improvements. A deeper analysis of the resulting models and additional experiments with adaptive procedures indicates that the rank and the Box-Cox transformation are able to improve the properties of the resultant distributions with respect to symmetry and normality of the residuals. Moreover, model-based effect plots document a higher discriminatory power obtained by the rank transformation.

关键词： Data transformation design and analysis of computer experiments Kriging parameter tuning sequential parameter optimization

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sequential parameter optimization for symbolic regression 12

Sequential parameter optimization for symbolic regression

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Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

作者： Thomas Bartz-Beielstein Oliver Flasch Martin Zaefferer Cologne University of Applied Sciences Cologne Germany

ISBN: (纸本)9781450311786

Modern Symbolic Regression (SR) engines are complex systems of many components, most of which require some form of parameterization. In this talk, we show how to apply sequential parameter optimization (SPO) as a rigorous method for finding near-optimal parameter settings for SR systems. As modern SR systems often offer alternative operator sets for population initialization, variation, and selection, we also demonstrate how to use modern Design of Experiments (DoE) methods to find problem-specific near-optimal SR system configurations, in addition to near-optimal parameterizations for each selected system component. The experimental design for SR can somehow be tricky, because of interactions in the parameter settings. Methods for handling configurations of parameters which depend on higher-level parameters will be presented. Our exposition is based on a simple framework for statistical sound, reproducible empirical research in SR.

关键词： sequential parameter optimization

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sequential parameter optimization for Multi-Objective Problems

Sequential Parameter Optimization for Multi-Objective Proble...

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2010 IEEE World Congress on Computational Intelligence

作者： Wessing, Simon Naujoks, Boris Tech Univ Dortmund Fac Comp Sci Computat Intelligence Grp Chair Algorithm Engn Dortmund Germany

ISBN: (纸本)9781424481262

Optimizing an algorithm's parameter set for evolutionary multi-objective optimization (EMO) algorithms is not performed regularly until now. However, it could have been learned from single-objective optimization that doing so yields remarkable improvements in algorithm's performance. Here, the sequential parameter optimization (SPO) framework is exemplarily applied to one EMO algorithm (EMOA) with different questions handled in different experiments. The main goal is to show the wide application area of such methods with a second, minor focus on the achievable improvements.

关键词： evolutionary computation evolutionary multi-objective optimization sequential parameter optimization

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sequential parameter optimization of an Evolution Strategy for the Design of Mold Temperature Control Systems

Sequential Parameter Optimization of an Evolution Strategy f...

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2010 IEEE World Congress on Computational Intelligence

作者： Biermann, Dirk Joliet, Raffael Michelitsch, Thomas Wagner, Tobias Tech Univ Dortmund Inst Machining Technol ISF D-44227 Dortmund Germany

ISBN: (纸本)9781424481262

sequential parameter optimization (SPO) is a popular model-assisted approach for tuning the parameters of metaheuristics, which is based on models from the Design and Analysis of Computer Experiments (DACE). Despite the indisputable success of SPO, some of the assumptions behind DACE, such as deterministic output and stationary covariance, do not hold for parameter optimization. Thus, an analysis of enhanced covariance kernels for the consideration of noise is performed. Furthermore, the effects of different sequential sampling strategies and an increasing number of replicates of each design on the quality of the models are discussed. To accomplish this, an Evolution Strategy (ES) is tuned on the real-world optimization problem of designing Mold Temperature Control Systems. Based on the results, recommendations for the ES parameters are provided, insights about the robustness of DACE with respect to the violations are made, and recommendations for appropriate combinations of sampling strategies and covariance kernels are derived.

关键词： computer experiments covariance kernels deterministic output evolution strategy evolutionary computation mold temperature control systems moulding sequential parameter optimization sequential sampling stationary covariance temperature control

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Joint sensing and power allocation in wideband cognitive radio networks

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TELECOMMUNICATION SYSTEMS 2016年第2期62卷 375-386页

作者： Li, Min Hei, Yongqiang Qiu, Zhuo Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China

This paper investigates the optimization problem in wideband cognitive radio networks. It focuses on jointly optimizing the decision threshold, sensing time and transmission power to maximize the aggregate throughput under the constraints of local interference, false-alarm probability, missed detection probability and maximal total power of each secondary user. sequential parameter optimization strategy is introduced to deal with different optimization parameters in the optimization problem. It starts with power allocation first, with the optimal power allocation vector obtained by adaptive power allocation strategy, the gold section search method combined with Taguchi algorithm is proposed to find the optimal sensing time with the optimal power allocation vector, and the optimal decision threshold with the optimal power allocation vector and sensing time is optimized through iterative process. Simulation results show that the proposed algorithm can achieve a better performance and is effective in solving such optimization problems.

关键词： Wideband cognitive radio networks sequential parameter optimization Adaptive power allocation Gold section search Taguchi algorithm

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How to create meaningful and generalizable results

How to create meaningful and generalizable results

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15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013

作者： Bartz-Beielstein, Thomas Zaefferer, Martin Naujoks, Boris *** Cologne University of Applied Sciences Germany

ISBN: (纸本)9781450319645

Q-1: How to generate test problems? Randomization! Objective Systematic approach Related to standard ANOVA Q-2: How to generalize results? Randomization! Artificial problems and natural problems treated in the same framework Confidence intervals (predictable algorithm behavior) Updates and additional material can be downloaded from ***.

关键词： mixed effect models sequential parameter optimization

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SPOT Applied to Non-Stochastic optimization Problems - An Experimental Study 12

SPOT Applied to Non-Stochastic Optimization Problems - An Ex...

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Bartz-Beielstein, Thomas Friese, Martina Naujoks, Boris Zaefferer, Martin Cologne Univ Appl Sci Fac Comp & Engn Sci D-51643 Gummersbach Germany

No abstract available.

ISBN: (纸本)9781450311786

No abstract available.

关键词： sequential parameter optimization

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Multi-Criteria optimization for Hard Problems under Limited Budgets 12

Multi-Criteria Optimization for Hard Problems under Limited ...

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Zaefferer, Martin Bartz-Beielstein, Thomas Friese, Martina Naujoks, Boris Flasch, Oliver Cologne Univ Appl Sci Fac Comp & Engn Sci D-51643 Gummersbach Germany

ISBN: (纸本)9781450311786

Many relevant industrial optimization tasks feature more than just one quality criterion. State-of-the-art multi-criteria optimization algorithms require a relatively large number of function evaluations (usually more than 10^5) to approximate Pareto fronts. Due to high cost or time consumption this large amount of function evaluations is not always available. Therefore, it is obvious to combine techniques such as sequential parameter optimization (SPO), which need a very small number of function evaluations only, with techniques from evolutionary multi-criteria optimization (EMO). In this paper, we show how EMO techniques can be efficiently integrated into the framework of the SPO Toolbox (SPOT). We discuss advantages of this approach in comparison to state-of-the-art optimizers. Moreover, with the resulting capability to allow competing objectives, the opportunity arises to not only aim for the best, but also for the most robust solution. Herein we present an approach to optimize not only the quality of the solution, but also its robustness, taking these two goals as objectives for multi-criteria optimization into account.

关键词： sequential parameter optimization Multi-Criteria optimization Robustness

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Advanced concepts for multi-objective evolutionary optimization in aircraft industry

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2011年第G10期225卷 1081-1096页

作者： Naujoks, B. Trautmann, H. Wessing, S. Weihs, C. TU Dortmund Univ Fac Comp Sci D-44221 Dortmund Germany Cologne Univ Appl Sci Fac Comp Sci & Engn Sci Gummersbach Germany TU Dortmund Univ Stat Fac D-44221 Dortmund Germany

Evolutionary (multi-objective optimization) algorithms (EMOAs) are widely accepted to be competitive optimization methods in industry today. However, normally only standard techniques are employed by the engineering experts. Here, it is shown how these standard techniques can be completed and improved with respect to interactivity to other tools, runtime, and parameterization. The coupling with metamodels serves as an example for the interactivity to other tools, while the online convergence detection relates to runtime, i.e. stopping criteria. Finally, sequential parameter optimization improves results focussing on parameter tuning. We show that invoking all these methods on their own already enhances EMOAs for aerodynamic applications. It is concluded with an outlook on how these methods might come together to foster aerospace applications and, at a time, widen the application area to multi-disciplinary optimization tasks.

关键词： evolutionary multi-objective optimization aerodynamic applications metamodel assistance stopping criteria convergence detection sequential parameter optimization parameter tuning

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Tuned Data Mining: A Benchmark Study on Different Tuners 11

Tuned Data Mining: A Benchmark Study on Different Tuners

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13th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Konen, Wolfgang Koch, Patrick Flasch, Oliver Bartz-Beielstein, Thomas Friese, Martina Naujoks, Boris Cologne Univ Appl Sci D-51643 Gummersbach Germany

ISBN: (纸本)9781450305570

The complex, often redundant and noisy data in real-world data mining (DM) applications frequently lead to inferior results when out-of-the-box DM models are applied. A tuning of parameters is essential to achieve high-quality results. In this work we aim at tuning parameters of the preprocessing and the modeling phase conjointly. The framework TDM (Tuned Data Mining) was developed to facilitate the search for good parameters and the comparison of different tuners. It is shown that tuning is of great importance for high-quality results. Surrogate-model based tuning utilizing the sequential parameter optimization Toolbox (SPOT) is compared with other tuners (CMA-ES, BFGS, LHD) and evidence is found that SPOT is well suited for this task. In benchmark tasks like the Data Mining Cup (DMC) tuned models achieve remarkably better ranks than their untuned counterparts.

关键词： parameter tuning Data Mining sequential parameter optimization

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