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2010 IEEE World Congress on Computational Intelligence

作者： Orito, Yukiko Sugizaki, Shota Yamamoto, Hisashi Tsujimura, Yasuhiro Kambayashi, Yasushi Hiroshima Univ Grad Sch Social Sci 1-2-1 Kagamiyama Hiroshima 7398525 Japan Nippon Inst Technol Dept Comp & Engn JP-3458501 Urawa Saitama Japan

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 genetic algorithms 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 genetic algorithm (GA).

关键词： evolutionary method genetic algorithms index fund rebalancing investment investment targets multidimensional problem portfolio optimization probabilistic model-building genetic algorithm probability rebalancing problem simulated annealing width histograms

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probabilistic model-building genetic algorithms 10

Probabilistic Model-Building Genetic Algorithms

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12th Annual genetic and Evolutionary Computation Conference (GECCO)

作者： Pelikan, Martin Univ Missouri St Louis Dept Math & Comp Sci Missouri Estimat Distribut Algorithms Lab St Louis MO USA

ISBN: (纸本)9781450300735

probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model 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 probabilistic model 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 probabilistic model-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.

关键词： bayesian network probabilistic model eda optimization genetic algorithm probabilistic model-building genetic algorithm estimation of distribution algorithm evolutionary computation learning

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probabilistic model-building genetic algorithms 08

Probabilistic model-building genetic algorithms

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10th Annual genetic and Evolutionary Computation Conference, GECCO 2008

作者： Pelikan, Martin Dept. of Math and Computer Science University of Missouri St. Louis United States

ISBN: (纸本)9781605581309

关键词： optimization probabilistic model estimation of distribution algorithm machine learning genetic algorithm pmbga eda probabilistic model-building genetic algorithm evolutionary computation

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Implementation of a probabilistic model- building co-evolutionary algorithm

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ARTIFICIAL LIFE AND ROBOTICS 2011年第3期16卷 373-377页

作者： Otani, Takahiro Arita, Takaya Nagoya Univ Grad Sch Informat Sci Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan

We propose an extended co-evolutionary algorithm (CA) with probabilistic model building (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.

关键词： Co-evolutionary algorithm probabilistic model-building genetic algorithm Intransitive numbers game

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An Implementation of probabilistic model-building Coevolutionary algorithm

An Implementation of Probabilistic Model-Building Coevolutio...

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16th International Symposium on Artificial Life and Robotics (AROB 16th '11)

作者： Otani, Takahiro Arita, Takaya Nagoya Univ Grad Sch Informat Sci Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan

ISBN: (纸本)9784990288051

We propose an extended coevolutionary algorithm (CA) with probabilistic model-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.

关键词： Coevolutionary algorithm probabilistic model-building genetic algorithm Intransitive numbers game

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A hybrid local search algorithm for scheduling real-world job shops with batch-wise pending due dates

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2012年第2期25卷 209-221页

作者： Zhang, Rui Wu, Cheng Nanchang Univ Sch Econ & Management Nanchang 330031 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

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.

关键词： Due date assignment Real-world job shop scheduling probabilistic model-building genetic algorithm Local search Weighted tardiness

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Positive definite nonparametric regression using an evolutionary algorithm with application to covariance function estimation 23

Positive definite nonparametric regression using an evolutio...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Kang, Myeongjong Texas A&M Univ College Stn TX 77843 USA

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.

关键词： covariance estimation shape constrained regression variogram analysis kernel smoothing probabilistic model-building genetic algorithm

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probabilistic distribution models for EDA-based GP 05

Probabilistic distribution models for EDA-based GP

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genetic and Evolutionary Computation Conference

作者： Yanai, Kohsuke Iba, Hitoshi Univ Tokyo Grad Sch Frontier Sci Dept Frontier Informat Kashiwa Chiba 2778561 Japan

ISBN: (纸本)1595930108

This paper proposes a novel technique for a program evolution based on probabilistic models. 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.

关键词： algorithms genetic programming program evolution evolutionary computing estimation of distribution algorithm probabilistic model-building genetic algorithm

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