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Hans-Georg Beyer, Bernhard Sendhoff, "Towards a steady-state analysis of an evolution strategy on a robust optimization problem with noise-induced multi-modality", IEEE Transactions on Evolutionary Computation, IEEE Press, 2017

Abstract

A steady state analysis of the optimization quality of a classical self-adaptive Evolution Strategy (ES) on a class of robust optimization problems is presented. A novel technique for calculating progress rates for non-quadratic noisy fitness landscapes is presented. This technique yields asymptotically exact results in the infinite population size limit. This technique is applied to a class of functions with noise-induced multi-modality. The ...



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Ran Cheng, Tobias Rodemann, Michael Fischer, Markus Olhofer, Yaochu Jin, "Evolutionary Many-objective Optimization of Hybrid Electric Vehicle Control: from General Optimization to Preference Articulation", IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE, pp. 97-111, 2017

Abstract

A key element in hybrid car design is the energy management controller that has to guarantee peak performance for an increasing number of conflicting objectives, e.g., fuel consumption, battery stress, emissions, noise, et al. Recently, a seven-objective controller model has been suggested to promote optimal controls of hybrid cars. Nowadays, such an optimization problem with more than three conflicting objectives is often known as a many-object...



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Hans-Georg Beyer, Bernhard Sendhoff, "Simplify Your Covariance Matrix Adaptation Evolution Strategy", IEEE Transactions on Evolutionary Computation, IEEE Press, 2017

Abstract

The standard Covariance Matrix Adaptation Evolution Strategy (CMA-ES) comprises two evolution paths, one for the learning of the mutation strength and one for the rank- 1 update of the covariance matrix. In this paper is is shown that one can approximately transform this algorithm in such a manner that one of the evolution paths and the covariance matrix itself disappear. That is, the covariance update and the covariance matrix square root...



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Andreas Richter, Jascha Achenbach, Stefan Menzel, Mario Botsch, "Multi-objective Representation Setups for Deformation-based Design Optimization", 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO), Springer, pp. 514-528, 2017

Abstract

The increase of complexity in virtual product design requires high-quality optimization algorithms capable to find the global parameter solution for a given problem. Population-based evolutionary design optimization targets to solve these kinds of application problems, offering efficient algorithms striving for high-quality solutions. The representation, which defines the encoding of the design and the mapping from parameter space to design s...



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Kaname Narukawa, Yu Setoguchi, Yuki Tanigaki, Markus Olhofer, Bernhard Sendhoff, Hisao Ishibuchi, "Preference representation using Gaussian functions on a hyperplane in evolutionary many-objective optimization", Soft Computing, Springer Berlin Heidelberg, pp. 2733-2757, 2016

Abstract

Many-objective optimization has attracted much attention in evolutionary multi-objective optimization (EMO). This is because EMO algorithms developed so far often degrade their search ability for optimize problems with four or more objectives, which are frequently referred to as many-objective problems. One of promising approaches to handle many objectives is to incorporate the preference of a decision maker (DM) into EMO algorithms. With the pre...



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Tobias Rodemann, Ken Nishikawa, "Can Evolutionary Algorithms Beat Dynamic Programming for Hybrid Car Control?", Lecture Notes in Computer Science (EvoApplications Part I), Springer, 2016

Abstract

Finding the best possible sequence of control actions for a hybrid car in order to minimize fuel consumption is a well-studied problem. A standard method is Dynamic Programming (DP) that is generally considered to provide solutions close to the global optimum in relatively short time. To our knowledge Evolutionary Algorithms (EAs) have so far not been used for this setting, due to the success of DP. In this work we compare DP and EA for a well-s...



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Daniel Sieger, Sergius Gaulik, Jascha Achenbach, Stefan Menzel, Mario Botsch, "Constrained Space Deformation Techniques for Design Optimization", Computer-Aided Design, Elsevier, pp. 40-51, 2016

Abstract

We present a novel shape deformation method for its use in design optimization tasks. Our space deformation technique based on moving least squares approximation improves upon existing approaches in crucial aspects: It offers the same level of modeling flexibility as surface-based deformations, but it is independent of the underlying geometry representation and therefore highly robust against defects in the input data. It overcomes the scalabil...



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Viktor Losing, Barbara Hammer, Heiko Wersing, "Choosing the Best Algorithm for an Incremental Learning Task", European Symposium on Artificial Neural Networks, i6doc, 2016

Abstract

Incremental and on-line learning gained recently more atten- tion especially in the context of big data and learning from data streams, conflicting with the traditional assumption of complete data availability. Even though plenty of different methods are available, it often remains unclear which of them is suitable for a specific task and how they perform in comparison to each other. We analyze the key properties of seven in- cremental meth...



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Ran Cheng, Yaochu Jin, Markus Olhofer, Bernhard Sendhoff, "A Reference Vector Guided Evolutionary Algorithm for Many-objective Optimization", IEEE Transactions on Evolutionary Computation, IEEE, pp. 773-791, 2016

Abstract

In evolutionary multi-objective optimization, maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms. In addition, it becomes increasingly important to incorporate user preferences because it will be less likely to achieve a representative subset of the Pareto optimal solutions using a limited population size as the number of objectives increases. This paper proposes ...



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Mariusz Bujny, Nikola Aulig, Markus Olhofer, Fabian Duddeck, "Evolutionary Level Set Method for Crashworthiness Topology Optimization", ECCOMAS Congress 2016, European Community on Computational Methods in Applied Sciences, 2016

Abstract

Vehicle crashworthiness design belongs to one of the most complex problems considered in the design optimization. Physical phenomena that are taken into account in crash simulations range from complex contact modeling to mechanical failure of materials. This results in high nonlinearity of the optimization problem and involves remarkable amount of numerical noise and discontinuities of the objective functions that are being optimized. Consequentl...



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