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 ...
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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.
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 probabili...
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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.
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 represent...
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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/).
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 meth...
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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.
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 b...
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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.
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 Al...
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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.
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 insta...
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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.
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 ...
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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.
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 c...
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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.
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