The portfolio optimization/rebalancing problem is to determine a proportion-weighted combination in a portfolio in order to achieve certain investment targets. For this problem, many researchers have used various evol...
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ISBN:
(纸本)9781424481262
The portfolio optimization/rebalancing problem is to determine a proportion-weighted combination in a portfolio in order to achieve certain investment targets. For this problem, many researchers have used various evolutionary methods and models such as geneticalgorithms and simulated annealing. On the other hand, the portfolio optimization/rebalancing problem can be viewed as a multi-dimensional problem because its solution is a proportion-weighted combination for the given assets. The previous works, however, have not taken into account the multi-dimensional aspect of the problem. In order to approach this problem from the multi-dimensional aspect, we propose a model based on the probabilistic model-building genetic algorithm with narrower width histograms (PMBGA-NWH), and then apply it to optimize the constrained index funds with the given rebalancing cost in this paper. In the numerical experiments, we show that our model has better ability to make optimal index funds than the traditional geneticalgorithm (GA).
probabilisticmodel-buildingalgorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilisticmodel of promising solutions and (2) sampling the built model to ge...
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ISBN:
(纸本)9781450300735
probabilisticmodel-buildingalgorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilisticmodel of promising solutions and (2) sampling the built model to generate new candidate solutions. PMBGAs are also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs).Replacing traditional crossover and mutation operators by building and sampling a probabilisticmodel of promising solutions enables the use of machine learning techniques for automatic discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. Using machine learning in optimization enables the design of optimization techniques that can automatically adapt to the given problem. There are many successful applications of PMBGAs, for example, Ising spin glasses in 2D and 3D, graph partitioning, MAXSAT, feature subset selection, forest management, groundwater remediation design, telecommunication network design, antenna design, and *** tutorial probabilisticmodel-building GAs will provide a gentle introduction to PMBGAs with an overview of major research directions in this area. Strengths and weaknesses of different PMBGAs will be discussed and suggestions will be provided to help practitioners to choose the best PMBGA for their problem.
作者:
Pelikan, Martin
Dept. of Math and Computer Science University of Missouri St. Louis United States
probabilisticmodel-buildingalgorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilisticmodel of promising solutions and (2) sampling the built model to ge...
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ISBN:
(纸本)9781605581309
probabilisticmodel-buildingalgorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilisticmodel of promising solutions and (2) sampling the built model to generate new candidate solutions. PMBGAs are also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs).Replacing traditional crossover and mutation operators by building and sampling a probabilisticmodel of promising solutions enables the use of machine learning techniques for automatic discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. Using machine learning in optimization enables the design of optimization techniques that can automatically adapt to the given problem. There are many successful applications of PMBGAs, for example, Ising spin glasses in 2D and 3D, graph partitioning, MAXSAT, feature subset selection, forest management, groundwater remediation design, telecommunication network design, antenna design, and *** tutorial probabilisticmodel-building GAs will provide a gentle introduction to PMBGAs with an overview of major research directions in this area. Strengths and weaknesses of different PMBGAs will be discussed and suggestions will be provided to help practitioners to choose the best PMBGA for their problem.
We propose an extended co-evolutionary algorithm (CA) with probabilisticmodelbuilding (CA-PMB) in order to improve the search performance of the CA. This article specifically describes an implementation of CA-PMB ca...
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We propose an extended co-evolutionary algorithm (CA) with probabilisticmodelbuilding (CA-PMB) in order to improve the search performance of the CA. This article specifically describes an implementation of CA-PMB called a co-evolutionary algorithm with population-based incremental learning (CA-PBIL), and analyzes the behavior of the algorithm through computational experiments using an intransitive numbers game as a benchmark problem. The experimental results show that desirable co-evolution may be inhibited by the over-specialization effect, and that the algorithm shows complex dynamics caused by the game's intransitivity. However, further experiments show that the intransitivity encourages desirable coevolution when a different learning rate is set for each population.
We propose an extended coevolutionary algorithm (CA) with probabilisticmodel-building (CA-PMB) in order to improve search performance of CA. This paper specifically describes an implementation of CA-PMB called coevol...
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ISBN:
(纸本)9784990288051
We propose an extended coevolutionary algorithm (CA) with probabilisticmodel-building (CA-PMB) in order to improve search performance of CA. This paper specifically describes an implementation of CA-PMB called coevolutionary algorithm with population-based incremental learning (CA-PBIL), and analyzes behavior of the algorithm through computational experiments using intransitive numbers game as a benchmark problem. The experimental results show that desirable coevolution may be inhibited by the over-specialization effect, and that the algorithm shows complex dynamics caused by the game's intransitivity. However, further experiments show that the intransitivity encourages desirable coevolution when a different learning rate is set for each population.
This paper aims at solving a real-world job shop scheduling problem with two characteristics, i.e., the existence of pending due dates and job batches. Due date quotation is an important decision process for contempor...
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This paper aims at solving a real-world job shop scheduling problem with two characteristics, i.e., the existence of pending due dates and job batches. Due date quotation is an important decision process for contemporary companies that adopt the MTO (make to order) strategy. Although the assignment of due dates is usually performed separately with production scheduling, there exist strong interactions between the two tasks. Therefore, we integrate these two decisions into one optimization model. Meanwhile, each order placed by the customer defines a batch of jobs, for which the same due date should be set. Thus, the completion times of these jobs should be close to one another in order to reduce waiting time and cost. For this purpose, we propose a dispatching rule to synchronize their manufacturing progresses. A two-stage local search algorithm based on the PMBGA (probabilistic model-building genetic algorithm) and parameter perturbation is proposed to solve the integrated scheduling problem and its superiority is revealed by the applications to a real-world mechanical factory. (C) 2011 Elsevier Ltd. All rights reserved.
We propose a novel nonparametric regression framework subject to the positive definiteness constraint. It offers a highly modular approach for estimating covariance functions of stationary processes. Our method can im...
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ISBN:
(纸本)9798400701191
We propose a novel nonparametric regression framework subject to the positive definiteness constraint. It offers a highly modular approach for estimating covariance functions of stationary processes. Our method can impose positive definiteness, as well as isotropy and monotonicity, on the estimators, and its hyperparameters can be decided using cross validation. We define our estimators by taking integral transforms of kernel-based distribution surrogates. We then use the iterated density estimation evolutionary algorithm, a variant of estimation of distribution algorithms, to fit the estimators. We also extend our method to estimate covariance functions for point-referenced data. Compared to alternative approaches, our method provides more reliable estimates for long-range dependence. Several numerical studies are performed to demonstrate the efficacy and performance of our method. Also, we illustrate our method using precipitation data from the Spatial Interpolation Comparison 97 project.
This paper proposes a novel technique for a program evolution based on probabilisticmodels. In the proposed method, two probabilistic distribution models with probabilistic dependencies between variables are used tog...
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ISBN:
(纸本)1595930108
This paper proposes a novel technique for a program evolution based on probabilisticmodels. In the proposed method, two probabilistic distribution models with probabilistic dependencies between variables are used together. We empirically comfirm that our proposed method has higher search performance. Thereafter, we discuss the effectiveness of its distribution models.
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