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Simulation Optimization Using the Cross-Entropy Method with Optimal Computing Budget Allocation

ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION 2010年第1期20卷 4-4页

作者： He, Donghai Lee, Loo Hay Chen, Chun-Hung Fu, Michael C. Wasserkrug, Segev George Mason Univ Dept Syst Engn & Operat Res Fairfax VA 22030 USA Natl Univ Singapore Dept Ind & Syst Engn Singapore 119077 Singapore Univ Maryland Robert H Smith Sch Business Decis Operat & Informat Technol Dept College Pk MD 20742 USA IBM Haifa Res Lab Haifa Israel

We propose to improve the efficiency of simulation optimization by integrating the notion of optimal computing budget allocation into the Cross-Entropy (CE) method, which is a global optimization search approach that iteratively updates a parameterized distribution from which candidate solutions are generated. This article focuses on continuous optimization problems. In the stochastic simulation setting where replications are expensive but noise in the objective function estimate could mislead the search process, the allocation of simulation replications can make a significant difference in the performance of such global optimization search algorithms. A new allocation scheme is developed based on the notion of optimal computing budget allocation. The proposed approach improves the updating of the sampling distribution by carrying out this computing budget allocation in an efficient manner, by minimizing the expected mean-squared error of the CE weight function. Numerical experiments indicate that the computational efficiency of the CE method can be substantially improved if the ideas of computing budget allocation are applied.

关键词： Simulation optimization computing budget allocation cross-entropy method estimation of distribution algorithms

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Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2023年第2期24卷 17-17页

作者： Wittenberg, David Rothlauf, Franz Gagne, Christian Johannes Gutenberg Univ Mainz Dept Dermatol Mainz Rhineland Palat Germany Laval Univ Quebec City PQ Canada

Denoising autoencoder genetic programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming approach that uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming. At each generation, the idea is to capture promising properties of the parent population in a probabilistic model and to use corruption to transfer variations of these properties to the offspring. This work studies the influence of corruption and sampling steps on search. Corruption partially mutates candidate solutions that are used as input to the model, whereas the number of sampling steps defines how often we re-use the output during model sampling as input to the model. We study the generalization of the royal tree problem, the Airfoil problem, and the Pagie-1 problem, and find that both corruption strength and the number of sampling steps influence exploration and exploitation in search and affect performance: exploration increases with stronger corruption and lower number of sampling steps. The results indicate that both corruption and sampling steps are key to the success of the DAE-GP: it permits us to balance the exploration and exploitation behavior in search, resulting in an improved search quality. However, also selection is important for exploration and exploitation and should be chosen wisely.

关键词： Genetic programming estimation of distribution algorithms Probabilistic model-building Denoising autoencoders

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Experimental analysis of a statistical multiploid genetic algorithm for dynamic environments

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2022年 35卷

作者： Gazioglu, Emrullah Etaner-Uyar, A. Sima Istanbul Tech Univ Comp & Informat Dept Istanbul Turkey Fatih Sultan Mehmet Vakif Univ Comp Engn Dept Istanbul Turkey

Dynamic environments are still a big challenge for optimization algorithms. In this paper, a Genetic Algorithm using both Multiploid representation and the Bayesian Decision method is proposed. By Multiploid representation, an implicit memory scheme is introduced to transfer useful information to the next generations. In this representation, there are more than one genotypes and only one phenotype. The phenotype values are determined based on the corresponding genotypes values. To determine phe-notype values, the well-known Bayesian Optimization Algorithm (BOA) has been injected into our algo-rithm to create a Bayes Network by using the previous population to exploit interactions between variables. With this algorithm, we have solved the well-known Dynamic Knapsack Problem (DKP) with 100, 250, and 500 items. Also, we have compared our algorithm with the most recent algorithm in the literature by using the DKP with 100 items. Experiments have shown that the proposed algorithm is effi-cient and faster than the peer algorithms in the manner of tracking moving optima without using an explicit memory scheme. In conclusion, using relationships between variables within the optimization algorithms is useful when concerning dynamic environments.(c) 2022 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： optimization genetic algorithm evolutionary computation dynamic environments estimation of distribution algorithms Bayesian Optimization Algorithm

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An Enhanced Nested Partitions Method

An Enhanced Nested Partitions Method

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2008 IEEE International Conference on Automation Science and Engineering (CASE 2008), vol.1

作者： Jin Sun Qian-chuan Zhao Peter B. Luh Center for Intelligent and Networked Systems Department of Automation TNLIST Tsinghua University Beijing China

Combinatorial optimization problems arise in many applications such as task assignment, facility location, and elevator scheduling. One powerful method to address those difficult problems is the nested partitions method (NP). This method, however, cannot use historical information because it is required that the sampling of different iterations should be independent in order to guarantee global convergence. In this paper, the convergence property of the NP method is enhanced by relaxing the independent sampling requirement with a much milder one so that historical information can be utilized without impairing algorithm convergence. Novel insights about how to effectively utilize historical information are obtained by representing the NP method in the viewpoint of the estimation of distribution algorithms (EDAs). The convergence result of the enhanced NP method is further extended to derive global convergence for a large class of population-based methods, population distribution-based methods.

关键词： Combinatorial optimization An enhanced nested partitions method estimation of distribution algorithms Population distribution-based methods

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iBOA: the incremental bayesian optimization algorithm 08

iBOA: the incremental bayesian optimization algorithm

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Proceedings of the 10th annual conference on Genetic and evolutionary computation

作者： Martin Pelikan Kumara Sastry David E. Goldberg University of Missouri in St. Louis St. Louis MO USA University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (纸本)9781605581309

This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unrestricted Bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.

关键词： bayesian optimization algorithm estimation of distribution algorithms evolutionary computation iBOA incremental boa BOA incremental model update EDA

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Designing EDAs by using the elitist convergent EDA concept and the boltzmann distribution 08

Designing EDAs by using the elitist convergent EDA concept a...

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Proceedings of the 10th annual conference on Genetic and evolutionary computation

作者： Sergio Ivvan Valdez Arturo Hernandez-Aguirre Salvador Botello-Rionda Centre for Research in Mathematics A.C. Guanajuato Mexico

ISBN: (纸本)9781605581309

This paper presents a theoretical definition for designing EDAs called Elitist Convergent estimation of distribution Algorithm (ECEDA), and a practical implementation: the Boltzmann Univariate Marginal distribution Algorithm (BUMDA). This proposal computes a Gaussian model which approximates a Boltzmann distribution via the minimization of the Kullback Leibler divergence. The resulting approach needs only one parameter: the population size. A set of problems is presented to show advantages and comparative performance of this approach with state of the art continuous EDAs.

关键词： performance analysis kullback-leibler divergence boltzmann distribution estimation of distribution algorithms

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A Unified Optimization Framework for Population-based Methods

A Unified Optimization Framework for Population-based Method...

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2008 IEEE International Conference on Automation Science and Engineering (CASE 2008), vol.1

作者： Jin Sun Qian-chuan Zhao Peter B. Luh Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing China

Combinatorial optimization problems arise in many applications such as task assignment, facility location, and elevator scheduling. A wide variety of population-based solution methods have been developed, either instance-based (e.g., genetic algorithm (GA) and particle swarm optimization (PSO)) or model-based (e.g., ant colony optimization (ACO) and estimation of distribution algorithms (EDAs)). Their various mechanisms make it difficult to analyze and compare these methods and to extend the advancement in one method to another. To this end, a unified optimization framework towards representing these seemingly different methods is established as iteratively sampling and updating of a population distribution. This framework is then innovatively instantiated with PSO from the instance-based category and EDA from the model-based category. Finally, the possible use and the finite time performance analysis of the unified framework are discussed.

关键词： Population-based optimization methods A unified optimization framework Particle swarm optimization estimation of distribution algorithms

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HYBRID MUSIC RECOMMENDER USING CONTENT-BASED AND SOCIAL INFORMATION

HYBRID MUSIC RECOMMENDER USING CONTENT-BASED AND SOCIAL INFO...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Paulo Chiliguano Gyorgy Fazekas Queen Mary University of London School of Electronic Engineering and Computer Science Mile End Road London E1 4NS UK

ISBN: (纸本)9781479999897

Internet resources available today, including songs, albums, playlists or podcasts, that a user cannot discover if there is not a tool to filter the items that the user might consider relevant. Several recommendation techniques have been developed since the Internet explosion to achieve this filtering task. In an attempt to recommend relevant songs to users, we propose an hybrid recommender that considers real-world users information and high-level representation for audio data. We use a deep learning technique, convolutional deep neural networks, to represent an audio segment in a n-dimensional vector, whose dimensions define the probability of the segment to belong to a specific music genre. To capture the listening behavior of a user, we investigate a state-of-the-art technique, estimation of distribution algorithms. The designed hybrid music recommender outperforms the predictions compared with a traditional content-based recommender.

关键词： Deep Learning Convolutional Neural Networks estimation of distribution algorithms Recommender Systems MIR Recommender systems Allocation Algorithm Consumers HYBRID Songs Multiple signal classification audio data Online resources Music information retrieval n-dimensional Music Albums

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Using symmetry and evolutionary search to minimize sorting networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Vinod K. Valsalam Risto Miikkulainen Google MPI for Intelligent Systems Department of Computer Sciences The University of Texas at Austin Austin TX

Sorting networks are an interesting class of parallel sorting algorithms with applications in multiprocessor computers and switching networks. They are built by cascading a series of comparison-exchange units called comparators. Minimizing the number of comparators for a given number of inputs is a challenging optimization problem. This paper presents a two-pronged approach called Symmetry and Evolution based Network Sort Optimization (SENSO) that makes it possible to scale the solutions to networks with a larger number of inputs than previously possible. First, it uses the symmetry of the problem to decompose the minimization goal into subgoals that are easier to solve. Second, it minimizes the resulting greedy solutions further by using an evolutionary algorithm to learn the statistical distribution of comparators in minimal networks. The final solutions improve upon half-century of results published in patents, books, and peer-reviewed literature, demonstrating the potential of the SENSO approach for solving difficult combinatorial problems.

关键词： combinatorial optimization estimation of distribution algorithms evolution sorting networks symmetry

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