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检索条件"主题词=Estimation-of-distribution algorithm"

共 24 条记录，以下是11-20 订阅

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Comparative Mixing for DSMGA-II

Comparative Mixing for DSMGA-II

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Komarnicki, Marcin M. Przewozniczek, Michal W. Durda, Tomasz M. Wroclaw Univ Sci & Technol Dept Computat Intelligence Wroclaw Poland

ISBN: (纸本)9781450371285

Dependency Structure Matrix Genetic algorithm-II (DSMGA-II) is a recently proposed optimization method that builds the linkage model on the base of the Dependency Structure Matrix (DSM). This model is used during the Optimal Mixing (OM) operators, such as the Restricted Mixing (RM) and the Back Mixing (BM). DSMGA-II was shown to solve theoretical and real-world optimization problems effectively. In this paper, we show that the effectiveness of DSMGA-II and its improved version, namely Two-edge Dependency Structure Matrix Genetic algorithm-II (DSMGA-IIe), is relatively low for NK-landscape problems. Thus, we propose the Comparative Mixing (CM) operator that extends the RM operator. The CM operator modifies the linkage information obtained from the DSM-based linkage model by comparing the receiver individual with a randomly selected member of the population. Such modification enables DSMGA-II to solve NK-landscape problems effectively and does not limit DSMGA-II performance on most problems for which it was already shown effective.

关键词： Genetic algorithm estimation-of-distribution algorithm Linkage Learning Model Building

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Two approaches of using heavy tails in high dimensional EDA 14

Two approaches of using heavy tails in high dimensional EDA

引用

14th IEEE International Conference on Data Mining (IEEE ICDM)

作者： Sanyang, Momodou L. Muehlbrandt, Hanno Kaban, Ata Univ Birmingham Sch Comp Sci Edgbaston B15 2TT W Midlands England

ISBN: (纸本)9781479942749

We consider the problem of high dimensional black-box optimisation via estimation of distribution algorithms (EDA). The Gaussian distribution is commonly used as a search operator in most of the EDA methods. However there are indications in the literature that heavy tailed distributions may perform better due to their higher exploration capabilities. Univariate heavy tailed distributions were already proposed for high dimensional problems. In 2D problems it has been reported that a multivariate heavy tailed (such as Cauchy) search distribution is able to blend together the strengths of multivariate modelling with a high exploration power. In this paper, we study whether a similar scheme would work well in high dimensional search problems. To get around of the difficulty of multivariate model building in high dimensions we employ a recently proposed random projections (RP) ensemble based approach which we modify to get samples from a multivariate Cauchy using the scale-mixture representation of the Cauchy distribution. Our experiments show that the resulting RP-based multivariate Cauchy EDA consistently improves on the performance of the univariate Cauchy search distribution. However, intriguingly, the RP-based multivariate Gaussian EDA has the best performance among these methods. It appears that the highly explorative nature of the multivariate Cauchy sampling is exacerbated in high dimensional search spaces and the population based search loses its focus and effectiveness as a result. Finally, we present an idea to increase exploration while maintaining exploitation and focus by using the RP-based multivariate Gaussian EDA in which the RP matrices are drawn with i.i.d. heavy tailed entries. This achieves improved performance and is competitive with the state of the art.

关键词： Gaussian distribution learning (artificial intelligence) matrix algebra sampling methods Cauchy distribution RP matrix estimation-of-distribution algorithm heavy tailed distribution high dimensional EDA high dimensional black-box optimisation multivariate Cauchy sampling multivariate heavy tailed search distribution multivariate modelling random projections ensemble based approach Buildings Covariance matrices Educational institutions Optimization Search problems Sociology estimation of distribution algorithm Multivariate Cauchy EDA Multivariate Gaussian EDA Random Projection Gaussian distribution tabu search European Defence Agency electronic design automation Electronic design automation and methodology EDA gene Educational institutions Sociology matrix algebra sampling methods Buildings Cauchy Allocation algorithm Multivariate DIMENSIONAL

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Comparison of Cauchy EDA and Rosenbrock's algorithms on the BBOB Noiseless Testbed 10

Comparison of Cauchy EDA and Rosenbrock's Algorithms on the ...

引用

12th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Posik, Petr Czech Tech Univ Fac Elect Engn Dept Cybernet Prague 16627 6 Czech Republic

ISBN: (纸本)9781450300735

estimation-of-distribution algorithm equipped with Cauchy distribution (Cauchy EDA) is compared with Rosenbrock's local search algorithm. Both algorithms were already presented at the 2009 black-box optimization benchmarking workshop where Cauchy EDA usually ranked better than Rosenbrock's algorithm. This paper compares them in more detail and adds to the understanding of their key differences.

关键词： Benchmarking Black-box optimization estimation-of-distribution algorithm Cauchy distribution Rosenbrock's algorithm

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A Tight Runtime Analysis for the cGA on Jump Functions-EDAs Can Cross Fitness Valleys at No Extra Cost

A Tight Runtime Analysis for the cGA on Jump Functions-EDAs ...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Doerr, Benjamin CNRS Ecole Polytech Lab Informat LIX Palaiseau France

ISBN: (纸本)9781450361118

We prove that the compact genetic algorithm (cGA) with hypothetical population size mu = Omega(root n log n) boolean AND poly(n) with high probability finds the optimum of any n-dimensional jump function with jump size k < 1/20 In n in O(mu root n) iterations. Since it is known that the cGA with high probability needs at least Omega(mu root n +n log n) iterations to optimize the unimodal ONEMAX function, our result shows that the cGA in contrast to most classic evolutionary algorithms here is able to cross moderate-sized valleys of low fitness at no extra cost. Our runtime guarantee improves over the recent upper bound 0(mu n(1.5) log n) valid for mu= Omega(n(3.5+epsilon)) of Hasenohrl and Sutton (GECCO 2018). For the best choice of the hypothetical population size, this result gives a runtime guarantee of O(n(5+epsilon)), whereas ours gives O(n log n). We also provide a simple general method based on parallel runs that, under mild conditions, (i) overcomes the need to specify a suitable population size, but gives a performance close to the one stemming from the best-possible population size, and (ii) transforms EDAs with high-probability performance guarantees into EDAs with similar bounds on the expected runtime.

关键词： estimation-of-distribution algorithm theory runtime analysis

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Two-edge Graphical Linkage Model for DSMGA-II 17

Two-edge Graphical Linkage Model for DSMGA-II

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Chen, Ping-Lin Peng, Chun-Jen Lu, Chang-Yi Yu, Tian-Li Natl Taiwan Univ Dept Elect Engn Taiwan Evolutionary Intelligence Lab Taipei Taiwan

ISBN: (纸本)9781450349208

DSMGA-II, a model-based genetic algorithm, is capable of solving optimization problems via exploiting sub-structures of the problem. In terms of number of function evaluations (NFE), DSMGA-II has shown superior optimization ability to LT-GOMEA and hBOA on various benchmark problems as well as real-world problems. This paper proposes a two-edge graphical linkage model, which customizes recombination masks for each receiver according to its alleles, to further improve the performance of DSMGA-II. The new linkage model is more expressive than the original dependency structure matrix (DSM), providing far more possible linkage combinations than the number of solutions in the search space. To reduce unnecessary function evaluations, the two-edge model is used along with the supply bounds from the original DSM. Some new techniques are also proposed to enhance the model selection efficiency. Combining these proposed techniques, the empirical results show an average of 12.2% NFE reduction on eight benchmark problems compared with the original DSMGA-II.

关键词： Genetic algorithm estimation-of-distribution algorithm Linkage Learning Model Building

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The Univariate Marginal distribution algorithm Copes Well with Deception and Epistasis 1

引用

20th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： Doerr, Benjamin Krejca, Martin S. Inst Polytech Paris Lab Informat LIX Ecole Polytech CNRS Palaiseau France Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (数字)9783030436803

ISBN: (纸本)9783030436797;9783030436803

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. 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 lambda(n(2) +2e ln n) fitness evaluations. Since an offspring population size lambda of order n log n can prevent genetic drift, the UMDA can solve the DLB problem with O(n(2) log n) fitness evaluations. In contrast, for classic evolutionary algorithms no better run time guarantee than O(n(3)) is known, so our result rather suggests that the UMDA can cope well with deception and epistatis. 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.

关键词： estimation-of-distribution algorithm Univariate marginal distribution algorithm Run time analysis Epistasis Theory

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Faster Optimization Through Genetic Drift 18th

Faster Optimization Through Genetic Drift

引用

18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Florescu, Cella Kaufmann, Marc Lengler, Johannes Schaller, Ulysse Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9783031700705;9783031700712

The compact Genetic algorithm (cGA), parameterized by its hypothetical population size K, offers a low-memory alternative to evolving a large offspring population of solutions. It evolves a probability distribution, biasing it towards promising samples. For the classical benchmark ONEMAX, the cGA has two different modes of operation: a conservative one with small step sizes Theta(1/(root n log n)), which is slow but prevents genetic drift, and an aggressive one with large step sizes Theta(1/ log n), in which genetic drift leads to wrong decisions, but those are corrected efficiently. On ONEMAX, an easy hill-climbing problem, both modes lead to optimization times of Theta(n log n) and are thus equally efficient. In this paper we study how both regimes change when we replace ONEMAX by the harder hill-climbing problem DYNAMIC BINVAL. It turns out that the aggressive mode is not affected and still yields quasi-linear runtime O(n polylog n). However, the conservative mode becomes substantially slower, yielding a runtime of Omega(n(2)), since genetic drift can only be avoided with smaller step sizes of O(1/n). We complement our theoretical results with simulations.

关键词： compact Genetic algorithm Genetic Drift estimation-of-distribution algorithm Dynamic Binary Value

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Comparison of Cauchy EDA and G3PCX algorithms on the BBOB Noiseless Testbed 10

Comparison of Cauchy EDA and G3PCX Algorithms on the BBOB No...

引用

12th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Posik, Petr Czech Tech Univ Fac Elect Engn Dept Cybernet Prague 16627 6 Czech Republic

ISBN: (纸本)9781450300735

estimation-of-distribution algorithm equipped with Cauchy sampling distribution is compared with the generalized generation gap algorithm with parent centric crossover. Both algorithms were already presented at the 2009 black-box optimization benchmarking workshop where they often showed similar performance. This paper compares them in more detail and adds to the understanding of their key features and differences.

关键词： Benchmarking Black-box optimization Generalized generation gap Parent-centric crossover estimation-of-distribution algorithm Cauchy distribution

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Stochastic Local Search in Continuous Domains Questions to be Answered When Designing A Novel algorithm 10

Stochastic Local Search in Continuous Domains Questions to b...

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12th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Posik, Petr Czech Tech Univ Fac Elect Engn Dept Cybernet Prague 16627 6 Czech Republic

ISBN: (纸本)9781450300735

Several population-based methods (with origins in the world of evolutionary strategies and estimation-of-distribution algorithms) for black-box optimization in continuous domains are surveyed in this article. The similarities and differences among them are emphasized and it is shown that they all can be described in a common framework of stochastic local search-a class of methods previously defined mainly for combinatorial problems. Based on the lessons learned from the surveyed algorithms, a set of algorithm features (or, questions to be answered) is extracted. An algorithm designer can take advantage of these features and by deciding on each of them, she can construct a novel algorithm. A few examples in this direction are shown.

关键词： Black-box optimization estimation-of-distribution algorithm Evolutionary strategy Covariance matrix adaptation Premature convergence Probabilistic modeling Gaussian distribution Cauchy distribution

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On measuring and improving the quality of linkage learning in modern evolutionary algorithms applied to solve partially additively separable problems

On measuring and improving the quality of linkage learning i...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Przewozniczek, Michal W. Frej, Bartosz Komarnicki, Marcin M. Wroclaw Univ Sci & Techn Dep Computat Intelligence Wroclaw Poland Wroclaw Univ Sci & Techn Fac Pure & Appl Math Wroclaw Poland

ISBN: (纸本)9781450371285

Linkage learning is frequently employed in modern evolutionary algorithms. High linkage quality may be the key to an evolutionary method's effectiveness. Similarly, the faulty linkage may be the reason for its poor performance. Many state-of-the-art evolutionary methods use a Dependency Structure Matrix (DSM) to obtain linkage. In this paper, we propose a quality measure for DSM. Based on this measure, we analyze the behavior of modern evolutionary methods. We show the dependency between the linkage and the effectiveness of the considered methods. Finally, we propose a framework that improves the quality of the linkage.

关键词： Linkage Quality Measurement Genetic algorithm estimation-of-distribution algorithm Linkage Learning Model Building

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