The Experts below are selected from a list of 70947 Experts worldwide ranked by ideXlab platform
Martin S Krejca - One of the best experts on this subject based on the ideXlab platform.
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a simplified run time analysis of the univariate Marginal Distribution algorithm on leadingones
Theoretical Computer Science, 2021Co-Authors: Benjamin Doerr, Martin S KrejcaAbstract:Abstract With elementary means, we prove a stronger run time guarantee for the univariate Marginal Distribution algorithm (UMDA) optimizing the LeadingOnes benchmark function in the desirable regime with low genetic drift. If the population size is at least quasilinear, then, with high probability, the UMDA samples the optimum in a number of iterations that is linear in the problem size divided by the logarithm of the UMDA's selection rate. This improves over the previous guarantee, obtained by Dang and Lehre (2015) via the deep level-based population method, both in terms of the run time and by demonstrating further run time gains from small selection rates. Under similar assumptions, we prove a lower bound that matches our upper bound up to constant factors.
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lower bounds on the run time of the univariate Marginal Distribution algorithm on onemax
Theoretical Computer Science, 2020Co-Authors: Martin S Krejca, Carsten WittAbstract:Abstract The Univariate Marginal Distribution Algorithm (UMDA) – a popular estimation-of-Distribution algorithm – is studied from a run time perspective. On the classical OneMax benchmark function on bit strings of length n, a lower bound of Ω ( λ + μ n + n log n ) , where μ and λ are algorithm-specific parameters, on its expected run time is proved. This is the first direct lower bound on the run time of UMDA. It is stronger than the bounds that follow from general black-box complexity theory and is matched by the run time of many evolutionary algorithms. The results are obtained through advanced analyses of the stochastic change of the frequencies of bit values maintained by the algorithm, including carefully designed potential functions. These techniques may prove useful in advancing the field of run time analysis for estimation-of-Distribution algorithms in general.
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the univariate Marginal Distribution algorithm copes well with deception and epistasis
Genetic and Evolutionary Computation Conference, 2020Co-Authors: Benjamin Doerr, Martin S KrejcaAbstract:In their recent work, Lehre and Nguyen (FOGA 2019) show that the univariate Marginal Distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceptiveLeadingBlocks (DLB) problem. They conclude from this result that univariate EDAs have difficulties with deception and epistasis. In this work, we show that this negative finding is caused by an unfortunate choice of the parameters of the UMDA. When the population sizes are chosen large enough to prevent genetic drift, then the UMDA optimizes the DLB problem with high probability with at most λ(n/2 + 2e ln n) fitness evaluations. Since an offspring population size λ of order n log n can prevent genetic drift, the UMDA can solve the DLB problem with O(n2 log n) fitness evaluations. Together with the result of Lehre and Nguyen, our result for the first time rigorously proves that running EDAs in the regime with genetic drift can lead to drastic performance losses. This extended abstract summarizes our work "The Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis", which appeared in the Proceedings of Evolutionary Computation in Combinatorial Optimization (EvoCOP), 2020, pp. 51--66, and won the conference's best-paper award.
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the univariate Marginal Distribution algorithm copes well with deception and epistasis
European conference on Evolutionary Computation in Combinatorial Optimization, 2020Co-Authors: Benjamin Doerr, Martin S KrejcaAbstract:In their recent work, Lehre and Nguyen (FOGA 2019) show that the univariate Marginal Distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceivingLeadingBlocks (DLB) problem. They conclude from this result that univariate EDAs have difficulties with deception and epistasis.
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a simplified run time analysis of the univariate Marginal Distribution algorithm on leadingones
arXiv: Neural and Evolutionary Computing, 2020Co-Authors: Benjamin Doerr, Martin S KrejcaAbstract:With elementary means, we prove a stronger run time guarantee for the univariate Marginal Distribution algorithm (UMDA) optimizing the LeadingOnes benchmark function in the desirable regime with low genetic drift. If the population size is at least quasilinear, then, with high probability, the UMDA samples the optimum within a number of iterations that is linear in the problem size divided by the logarithm of the UMDA's selection rate. This improves over the previous guarantee, obtained by Dang and Lehre (2015) via the deep level-based population method, both in terms of the run time and by demonstrating further run time gains from small selection rates. With similar arguments as in our upper-bound analysis, we also obtain the first lower bound for this problem. Under similar assumptions, we prove that a bound that matches our upper bound up to constant factors holds with high probability.
Fábio E. Bisogno - One of the best experts on this subject based on the ideXlab platform.
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application of the univariate Marginal Distribution algorithm to mixed analogue digital circuit design and optimisation
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet EvoFIN EvoIASP EvoINTERACTION EvoMUSART EvoSTOC and EvoTransLog: Applications of Evolutionary C, 2009Co-Authors: Lyudmila Zinchenko, Matthias Radecker, Fábio E. BisognoAbstract:Design and optimisation of modern complex mixed analogue-digital circuits require new approaches to circuit sizing. In this paper, we present a novel approach based on the application of the univariate Marginal Distribution algorithm to circuit sizing at the system level. The results of automotive electronics circuits sizing indicate that all design requirements have been fulfilled in comparison with a human design. Experiments indicate that elitism increases the performance of the algorithm.
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A comparison of fitness function evaluation schedules for multi-objective univariate Marginal Distribution optimization of mixed analog-digital signal circuits
2008Co-Authors: Lyudmila Zinchenko, Matthias Radecker, Fábio E. BisognoAbstract:An increasing complexity of mixed analog-digital signal circuits requires optimization at higher hierarchical level. However, evolutionary optimization of mixed analog-digital signal circuits at the system level results in huge computational costs. A key to manage these computational complexities of evolutionary circuit design is an application of flexible fitness functions evaluation schedules. In this paper we compare the static, dynamic, and co-evolution fitness function evaluation schedules for multi-objective optimization of mixed analog-digital signal circuits at the system level on the base of the univariate Marginal Distribution algorithm. Experiments for our symmetry recognition circuit benchmark chosen indicate that the dynamic fitness function schedule is a good compromise between computational costs and optimization efficiency.
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multi objective univariate Marginal Distribution optimisation of mixed analogue digital signal circuits
Genetic and Evolutionary Computation Conference, 2007Co-Authors: Lyudmila Zinchenko, Matthias Radecker, Fábio E. BisognoAbstract:Design for specific customer service plays a crucial role for the majority of the market in modern electronics. However, adaptability to an individual customer results in increasing design costs. A key to manage these opposite requirements is a wide application of computer aided design tools for multi-objective optimisation of existing IP blocks. In this paper we introduce a new approach to multi-objective optimisation of mixed analogue-digital signal circuits on the base of the univariate Marginal Distribution algorithm. Practical illustration of the use of this approach is demonstrated for an industrial electronics application design. Experiments indicate that multi-objective optimisation of mixed analogue-digital signal circuits on the base of the univariate Marginal Distribution algorithm meets different design specifications.
Nikolai Leonenko - One of the best experts on this subject based on the ideXlab platform.
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simulation of levy driven ornstein uhlenbeck processes with given Marginal Distribution
Computational Statistics & Data Analysis, 2009Co-Authors: Emanuele Taufer, Nikolai LeonenkoAbstract:A simulation procedure for obtaining discretely observed values of Ornstein-Uhlenbeck processes with given (self-decomposable) Marginal Distribution is provided. The method proposed, based on inversion of the characteristic function, completely circumvents the problems encountered when trying to reproduce small jumps of Levy processes. Error bounds for the proposed procedure are provided and its performance is numerically assessed.
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simulation of levy driven ornstein uhlenbeck processes with given Marginal Distribution
Quaderni DISA, 2007Co-Authors: Emanuele Taufer, Nikolai LeonenkoAbstract:We provide a simulation procedure for obtaining discretely observed values of Ornstein-Uhlenbeck processes with given (self-decomposable) Marginal Distribution. The method proposed, based on inversion of the characteristic function, completely circumvent problems encountered when trying to reproduce small jumps of Levy processes. We provide error bounds for our procedure and asses numerically its performance.
Lyudmila Zinchenko - One of the best experts on this subject based on the ideXlab platform.
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application of the univariate Marginal Distribution algorithm to mixed analogue digital circuit design and optimisation
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet EvoFIN EvoIASP EvoINTERACTION EvoMUSART EvoSTOC and EvoTransLog: Applications of Evolutionary C, 2009Co-Authors: Lyudmila Zinchenko, Matthias Radecker, Fábio E. BisognoAbstract:Design and optimisation of modern complex mixed analogue-digital circuits require new approaches to circuit sizing. In this paper, we present a novel approach based on the application of the univariate Marginal Distribution algorithm to circuit sizing at the system level. The results of automotive electronics circuits sizing indicate that all design requirements have been fulfilled in comparison with a human design. Experiments indicate that elitism increases the performance of the algorithm.
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A comparison of fitness function evaluation schedules for multi-objective univariate Marginal Distribution optimization of mixed analog-digital signal circuits
2008Co-Authors: Lyudmila Zinchenko, Matthias Radecker, Fábio E. BisognoAbstract:An increasing complexity of mixed analog-digital signal circuits requires optimization at higher hierarchical level. However, evolutionary optimization of mixed analog-digital signal circuits at the system level results in huge computational costs. A key to manage these computational complexities of evolutionary circuit design is an application of flexible fitness functions evaluation schedules. In this paper we compare the static, dynamic, and co-evolution fitness function evaluation schedules for multi-objective optimization of mixed analog-digital signal circuits at the system level on the base of the univariate Marginal Distribution algorithm. Experiments for our symmetry recognition circuit benchmark chosen indicate that the dynamic fitness function schedule is a good compromise between computational costs and optimization efficiency.
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multi objective univariate Marginal Distribution optimisation of mixed analogue digital signal circuits
Genetic and Evolutionary Computation Conference, 2007Co-Authors: Lyudmila Zinchenko, Matthias Radecker, Fábio E. BisognoAbstract:Design for specific customer service plays a crucial role for the majority of the market in modern electronics. However, adaptability to an individual customer results in increasing design costs. A key to manage these opposite requirements is a wide application of computer aided design tools for multi-objective optimisation of existing IP blocks. In this paper we introduce a new approach to multi-objective optimisation of mixed analogue-digital signal circuits on the base of the univariate Marginal Distribution algorithm. Practical illustration of the use of this approach is demonstrated for an industrial electronics application design. Experiments indicate that multi-objective optimisation of mixed analogue-digital signal circuits on the base of the univariate Marginal Distribution algorithm meets different design specifications.
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application of the univariate Marginal Distribution algorithm to analog circuit design
NASA DoD Conference on Evolvable Hardware, 2002Co-Authors: Lyudmila Zinchenko, H Muhlenbein, V Kureichik, T MahnigAbstract:The approach to computer aided analog circuit design on the base of univariate algorithms was derived by analysing the mathematical principles behind recombination. A Bayesian prior used for the estimations of the probability Distribution is equivalent to having mutation for the genetic algorithms. In this paper the relation between a success rate and a mutation one is considered for analog circuit design. Practical illustration of the use of this approach is demonstrated for filter design. Experiments indicate that mutation and elitism increase the performance of the algorithms and decrease the dependence of the correct choice of the population size.
Yuri Suhov - One of the best experts on this subject based on the ideXlab platform.
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eigenfunctions in a two particle anderson tight binding model
Communications in Mathematical Physics, 2009Co-Authors: Victor Chulaevsky, Yuri SuhovAbstract:We establish the phenomenon of Anderson localisation for a quantum two-particle system on a lattice \({\mathbb{Z}^d}\) with short-range interaction and in presence of an IID external potential with sufficiently regular Marginal Distribution.
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eigenfunctions in a two particle anderson tight binding model
arXiv: Mathematical Physics, 2008Co-Authors: Victor Chulaevsky, Yuri SuhovAbstract:We establish the phenomenon of Anderson localisation for a quantum two-particle system on a d-dimensional lattice with short-range interaction and in presence of an IID external potential with sufficiently regular Marginal Distribution.
