The Experts below are selected from a list of 18552 Experts worldwide ranked by ideXlab platform
Yong Liang - One of the best experts on this subject based on the ideXlab platform.
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genetic algorithm with adaptive Elitist population strategies for multimodal function optimization
Applied Soft Computing, 2011Co-Authors: Yong Liang, Kwongsak LeungAbstract:This paper introduces a new technique called adaptive Elitist-population search method. This technique allows unimodal function optimization methods to be extended to efficiently explore multiple optima of multimodal problems. It is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and a novel direction dependent Elitist genetic operators. Incorporation of the new multimodal technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, we have integrated the new technique into Genetic Algorithms (GAs), yielding an Adaptive Elitist-population based Genetic Algorithm (AEGA). AEGA has been shown to be very efficient and effective in finding multiple solutions of complicated benchmark and real-world multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including rough and stepwise multimodal functions. Empirical results are also compared with other multimodal evolutionary algorithms from the literature, showing that AEGA generally outperforms existing approaches.
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adaptive Elitist population based genetic algorithm for multimodal function optimization
Genetic and Evolutionary Computation Conference, 2003Co-Authors: Kwongsak Leung, Yong LiangAbstract:This paper introduces a new technique called adaptive Elitist-population search method for allowing unimodal function optimization methods to be extended to efficiently locate all optima of multimodal problems. The technique is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and the novel Elitist genetic operators. Incorporation of the technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, genetic algorithms(GAs) have been endowed with the multimodal technique, yielding an adaptive Elitist-population based genetic algorithm(AEGA). The AEGA has been shown to be very efficient and effective in finding multiple solutions of the benchmark multimodal optimization problems.
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adaptive Elitist population based genetic algorithm for multimodal function optimization
Genetic and Evolutionary Computation Conference, 2003Co-Authors: Kwongsak Leung, Yong LiangAbstract:This paper introduces a new technique called adaptive Elitist-population search method for allowing unimodal function optimization methods to be extended to efficiently locate all optima of multimodal problems. The technique is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and the novel Elitist genetic operators. Incorporation of the technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, genetic algorithms(GAs) have been endowed with the multimodal technique, yielding an adaptive Elitist-population based genetic algorithm(AEGA). The AEGA has been shown to be very efficient and effective in finding multiple solutions of the benchmark multimodal optimization problems.
T. Meyarivan - One of the best experts on this subject based on the ideXlab platform.
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A fast and Elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation, 2002Co-Authors: Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T. MeyarivanAbstract:Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN3) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN2) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other Elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed
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a fast Elitist non dominated sorting genetic algorithm for multi objective optimisation nsga ii
Parallel Problem Solving from Nature, 2000Co-Authors: Samir Agrawal, Amrit Pratap, T. MeyarivanAbstract:Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN3) computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algorithm (we called it the Non-dominated Sorting GA-II or NSGA-II) which alleviates all the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN2) computational complexity is presented. Second, a selection operator is presented which creates a mating pool by combining the parent and child populations and selecting the best (with respect to fitness and spread) N solutions. Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other Elitist multi-objective EAs which pay special attention towards creating a diverse Pareto-optimal front. Because of NSGA-II's low computational requirements, Elitist approach, and parameter-less sharing approach, NSGA-II should find increasing applications in the years to come.
Kwongsak Leung - One of the best experts on this subject based on the ideXlab platform.
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genetic algorithm with adaptive Elitist population strategies for multimodal function optimization
Applied Soft Computing, 2011Co-Authors: Yong Liang, Kwongsak LeungAbstract:This paper introduces a new technique called adaptive Elitist-population search method. This technique allows unimodal function optimization methods to be extended to efficiently explore multiple optima of multimodal problems. It is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and a novel direction dependent Elitist genetic operators. Incorporation of the new multimodal technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, we have integrated the new technique into Genetic Algorithms (GAs), yielding an Adaptive Elitist-population based Genetic Algorithm (AEGA). AEGA has been shown to be very efficient and effective in finding multiple solutions of complicated benchmark and real-world multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including rough and stepwise multimodal functions. Empirical results are also compared with other multimodal evolutionary algorithms from the literature, showing that AEGA generally outperforms existing approaches.
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adaptive Elitist population based genetic algorithm for multimodal function optimization
Genetic and Evolutionary Computation Conference, 2003Co-Authors: Kwongsak Leung, Yong LiangAbstract:This paper introduces a new technique called adaptive Elitist-population search method for allowing unimodal function optimization methods to be extended to efficiently locate all optima of multimodal problems. The technique is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and the novel Elitist genetic operators. Incorporation of the technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, genetic algorithms(GAs) have been endowed with the multimodal technique, yielding an adaptive Elitist-population based genetic algorithm(AEGA). The AEGA has been shown to be very efficient and effective in finding multiple solutions of the benchmark multimodal optimization problems.
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adaptive Elitist population based genetic algorithm for multimodal function optimization
Genetic and Evolutionary Computation Conference, 2003Co-Authors: Kwongsak Leung, Yong LiangAbstract:This paper introduces a new technique called adaptive Elitist-population search method for allowing unimodal function optimization methods to be extended to efficiently locate all optima of multimodal problems. The technique is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and the novel Elitist genetic operators. Incorporation of the technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, genetic algorithms(GAs) have been endowed with the multimodal technique, yielding an adaptive Elitist-population based genetic algorithm(AEGA). The AEGA has been shown to be very efficient and effective in finding multiple solutions of the benchmark multimodal optimization problems.
Walter Kellermann - One of the best experts on this subject based on the ideXlab platform.
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nonlinear acoustic echo cancellation using Elitist resampling particle filter
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Mhd Modar Halimeh, Christian Huemmer, Walter KellermannAbstract:This paper considers an effective method for nonlinear acoustic echo cancellation (NL-AEC). More specifically, we model the nonlinear echo path by a latent state vector capturing the coefficients of a memoryless processor and a linear finite impulse response filter. To estimate the posterior probability distribution of the state vector, an Elitist particle filter based on evolutionary strategies (EPFES) has been proposed, which evaluates realizations of the latent state vector based on long-term fitness measures. This method includes a manually-tuned recursive calculation of the probabilities that the observation has been produced by the state-vector realizations. For avoiding this manual tuning, we introduce a new approach denoted as Elitist Resampling Particle Filtering (ERPF) which can also be shown to combine the advantages of the Sequential Importance Sampling Particle Filter (SIS-PF) and the Sequential Importance Sampling/Resampling Particle Filter (SIR-PF). This new approach allows universal use and leads to superior system identification performance compared to both the original EPFES as well as the SIR-PF, as verified for a simulated scenario and a real smartphone recording.
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estimating parameters of nonlinear systems using the Elitist particle filter based on evolutionary strategies
IEEE Transactions on Audio Speech and Language Processing, 2018Co-Authors: Christian Huemmer, Christian Hofmann, Roland Maas, Walter KellermannAbstract:In this paper, we present the Elitist particle filter based on evolutionary strategies EPFES as an efficient approach to estimate the statistics of a latent state vector capturing the relevant information of a nonlinear system. Similar to classical particle filtering, the EPFES consists of a set of particles and respective weights which represent different realizations of the latent state vector and their likelihood of being the solution of the optimization problem. As main innovation, the EPFES includes an evolutionary Elitist-particle selection scheme which combines long-term information with instantaneous sampling from an approximated continuous posterior distribution. In this paper, we propose two advancements of the previously published Elitist-particle selection process. Further, the EPFES is shown to be a generalization of the widely-used Gaussian particle filter and thus evaluated with respect to the latter: First, we consider the univariate nonstationary growth model with time-variant latent state variable to evaluate the tracking capabilities of the EPFES for instantaneously calculated particle weights. This is followed by addressing the problem of single-channel nonlinear acoustic echo cancellation as a challenging benchmark task for identifying an unknown system of large search space: the nonlinear acoustic echo path is modeled by a cascade of a parameterized preprocessor to model the loudspeaker signal distortions and a linear FIR filter to model the sound wave propagation and the microphone. By using long-term information, we highlight the efficacy of the well-generalizing EPFES in estimating the preprocessor parameters for a simulated scenario and a real smartphone recording. Finally, we illustrate similarities between the EPFES and evolutionary algorithms to outline future improvements by fusing the achievements of both fields of research.
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estimating parameters of nonlinear systems using the Elitist particle filter based on evolutionary strategies
arXiv: Machine Learning, 2016Co-Authors: Christian Huemmer, Christian Hofmann, Roland Maas, Walter KellermannAbstract:In this article, we present the Elitist particle filter based on evolutionary strategies (EPFES) as an efficient approach for nonlinear system identification. The EPFES is derived from the frequently-employed state-space model, where the relevant information of the nonlinear system is captured by an unknown state vector. Similar to classical particle filtering, the EPFES consists of a set of particles and respective weights which represent different realizations of the latent state vector and their likelihood of being the solution of the optimization problem. As main innovation, the EPFES includes an evolutionary Elitist-particle selection which combines long-term information with instantaneous sampling from an approximated continuous posterior distribution. In this article, we propose two advancements of the previously-published Elitist-particle selection process. Further, the EPFES is shown to be a generalization of the widely-used Gaussian particle filter and thus evaluated with respect to the latter for two completely different scenarios: First, we consider the so-called univariate nonstationary growth model with time-variant latent state variable, where the evolutionary selection of Elitist particles is evaluated for non-recursively calculated particle weights. Second, the problem of nonlinear acoustic echo cancellation is addressed in a simulated scenario with speech as input signal: By using long-term fitness measures, we highlight the efficacy of the well-generalizing EPFES in estimating the nonlinear system even for large search spaces. Finally, we illustrate similarities between the EPFES and evolutionary algorithms to outline future improvements by fusing the achievements of both fields of research.
Johannes Lengler - One of the best experts on this subject based on the ideXlab platform.
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the 1 1 Elitist black box complexity of leadingones
Algorithmica, 2018Co-Authors: Carola Doerr, Johannes LenglerAbstract:One important goal of black-box complexity theory is the development of complexity models allowing to derive meaningful lower bounds for whole classes of randomized search heuristics. Complementing classical runtime analysis, black-box models help us to understand how algorithmic choices such as the population size, the variation operators, or the selection rules influence the optimization time. One example for such a result is the $$\varOmega (n \log n)$$ lower bound for unary unbiased algorithms on functions with a unique global optimum (Lehre and Witt in Algorithmica 64:623–642, 2012), which tells us that higher arity operators or biased sampling strategies are needed when trying to beat this bound. In lack of analyzing techniques, such non-trivial lower bounds are very rare in the existing literature on black-box optimization and therefore remain to be one of the main challenges in black-box complexity theory. With this paper we contribute to our technical toolbox for lower bound computations by proposing a new type of information-theoretic argument. We regard the permutation- and bit-invariant version of LeadingOnes and prove that its $$(1+1)$$ Elitist black-box complexity is $$\varOmega (n^2)$$ , a bound that is matched by $$(1+1)$$ -type evolutionary algorithms. The $$(1+1)$$ Elitist complexity of LeadingOnes is thus considerably larger than its unrestricted one, which is known to be of order $$n\log \log n$$ (Afshani et al. in Lecture notes in computer science, vol 8066, pp 1–11. Springer, New York, 2013). The $$\varOmega (n^2)$$ lower bound does not rely on the fact that Elitist black-box algorithms are not allowed to make use of absolute fitness values. In contrast, we show that even if absolute fitness values are revealed to the otherwise Elitist algorithm, it cannot significantly profit from this additional information. Our result thus shows that for LeadingOnes the memory-restriction, together with the selection requirement, has a substantial impact on the best possible performance.
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Introducing Elitist Black-Box Models: When Does Elitist Behavior Weaken the Performance of Evolutionary Algorithms?
Evolutionary Computation, 2017Co-Authors: Carola Doerr, Johannes LenglerAbstract:Black-box complexity theory provides lower bounds for the runtime of black-box optimizers like evolutionary algorithms and other search heuristics and serves as an inspiration for the design of new genetic algorithms. Several black-box models covering different classes of algorithms exist, each highlighting a different aspect of the algorithms under considerations. In this work we add to the existing black-box notions a new Elitist black-box model, in which algorithms are required to base all decisions solely on (the relative performance of) a fixed number of the best search points sampled so far. Our Elitist model thus combines features of the ranking-based and the memoryrestricted black-box models with an enforced usage of truncation selection.
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introducing Elitist black box models when does Elitist behavior weaken the performance of evolutionary algorithms
Evolutionary Computation, 2016Co-Authors: Carola Doerr, Johannes LenglerAbstract:Black-box complexity theory provides lower bounds for the runtime of black-box optimizers like evolutionary algorithms and other search heuristics and serves as an inspiration for the design of new genetic algorithms. Several black-box models covering different classes of algorithms exist, each highlighting a different aspect of the algorithms under considerations. In this work we add to the existing black-box notions a new Elitist black-box model, in which algorithms are required to base all decisions solely on (the relative performance of) a fixed number of the best search points sampled so far. Our Elitist model thus combines features of the ranking-based and the memory-restricted black-box models with an enforced usage of truncation selection. We provide several examples for which the Elitist black-box complexity is exponentially larger than that of the respective complexities in all previous black-box models, thus showing that the Elitist black-box complexity can be much closer to the runtime of typi...
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Elitist Black-Box Models: Analyzing the Impact of Elitist Selection on the Performance of Evolutionary Algorithms
2015Co-Authors: Carola Doerr, Johannes LenglerAbstract:Black-box complexity theory provides lower bounds for the runtime %classes of black-box optimizers like evolutionary algorithms and serves as an inspiration for the design of new genetic algorithms. Several black-box models covering different classes of algorithms exist, each highlighting a different aspect of the algorithms under considerations. In this work we add to the existing black-box notions a new \emph{Elitist black-box model}, in which algorithms are required to base all decisions solely on (a fixed number of) the best search points sampled so far. Our model combines features of the ranking-based and the memory-restricted black-box models with Elitist selection. We provide several examples for which the Elitist black-box complexity is exponentially larger than that the respective complexities in all previous black-box models, thus showing that the Elitist black-box complexity can be much closer to the runtime of typical evolutionary algorithms. We also introduce the concept of $p$-Monte Carlo black-box complexity, which measures the time it takes to optimize a problem with failure probability at most p. Even for small $p$, the $p$-Monte Carlo black-box complexity of a function class F can be smaller by an exponential factor than its typically regarded Las Vegas complexity (which measures the expected time it takes to optimize F).