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Real options approach to evaluating genetic algorithms

APPLIED SOFT COMPUTING 2009年第3期9卷 896-905页

作者： Rimcharoen, Sunisa Sutivong, Daricha Chongstitvatana, Prabhas Chulalongkorn Univ Dept Ind Engn Bangkok 10330 Thailand Chulalongkorn Univ Dept Comp Engn Bangkok 10330 Thailand

The real options technique has emerged as an evaluation tool for investment under uncertainty. It explicitly recognizes future decisions, and the exercise strategy is based on the optimal decisions in future periods. This paper employs the optimal stopping policy derived from real options approach to analyze and evaluate genetic algorithms, specifically for the new branches namely estimation of distribution algorithms (EDAs). As an example, we focus on their simple class called univariate EDAs, which include the population-based incremental learning (PBIL), the univariate marginal distribution algorithm (UMDA), and the compact genetic algorithm (cGA). Although these algorithms are classified in the same class, the characteristics of their optimal stopping policy are different. These observations are useful in answering the question "which algorithm is suitable for a particular problem''. The results from the simulations indicate that the option values can be used as a quantitative measurement for comparing algorithms. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Real options estimation of distribution algorithm Optimal stopping time

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Quantitative optimization of interoperability during feature-based data exchange

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INTEGRATED COMPUTER-AIDED ENGINEERING 2016年第1期23卷 31-50页

作者： Zhang, D. J. He, F. Z. Han, S. H. Li, X. X. Wuhan Univ Sch Comp Sci & Technol Wuhan 430072 Hubei Peoples R China Korea Adv Inst Sci & Technol Div Ocean Syst Engn Daejeon South Korea Huazhong Agr Univ Coll Sci Wuhan Hubei Peoples R China

Sharing feature-based computer-aided design (CAD) models is a challenging problem that is frequently encountered among heterogeneous CAD systems. In this work, a new asymmetric strategy is presented to enrich the theory of feature-based interoperability, particularly when addressing a singular feature or singular sketch. This paper analyzes the semantic asymmetry singular feature interoperability (SA-SFI) and parameter asymmetry singular sketch interoperability (PA-SSI) in detail. We pay special attention to the problem of PA-SSI, which is universally significant in collaborative product development (CPD). The objective of PA-SSI is to develop an optimized model to exchange a singular sketch (spline) to ensure that the exchanged model both maintains high geometric fidelity and can be effectively edited in the target CAD system. The proposed method applies the estimation of distribution algorithm (EDA) to automatically solve this problem, and a Gaussian mixture model (GMM) is built according to the promising solutions. Furthermore, Hausdorff distance is adopted to calculate the fitness, and a local optimization operator is designed to enhance the global search capability of the population. Experimental results demonstrate that the proposed approach can maintain a sufficiently high geometric fidelity, and ensure that the exchanged model of the target CAD system can be parametrically edited.

关键词： CAD/CAE feature-based data exchange interoperability estimation of distribution algorithm hausdorif distance performance evaluation

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Improved Mutual Information Maximizing Input Clustering algorithm for Permutation Flow Shop Scheduling Problem

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SENSOR LETTERS 2012年第8期10卷 1862-1866页

作者： Gao, Huimin Wang, Xiaoping Jiaxing Univ Mech & Elect Engn Coll Jiaxing 314001 Peoples R China Jiaxing Univ Sch Business Jiaxing 314001 Peoples R China

Improved Mutual Information Maximizing Input Clustering algorithm is a kind of discrete estimation of distribution algorithm, which is convenient to solve permutation flow shop scheduling problem. In this paper, the encoding mode and probability model are improved, new individual strategy is proposed, greedy algorithm is introduced at the initial phase of the probability matrix, and crossover operator, mutation operator, insert operator and swap operator are adopted during the process of evolution, dynamic adjusted method is employed to determine the population size. These improvements gurantee the population diversity even in small population. Experiment results show that the improved Mutual Information Maximizing Input Clustering algorithm is effective and stable.

关键词： Mutual Information Maximizing Input Clustering algorithm Permutation Flow Shop Scheduling Problem estimation of distribution algorithm Probability Matrix

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Duple-EDA and sample density balancing

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Science in China(Series F) 2009年第9期52卷 1640-1650页

作者： CAI YunPeng XU Hua SUN XiaoMin JIA PeiFa LIU ZeHua State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing 100084 China School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore

In this paper, a new method is proposed to overcome the problem of local optima traps in a class of evolutionary algorithms, called estimation of distribution algorithms （EDAs）, in real-valued function optimization. The Duple-EDA framework is proposed in which not only the current best solutions but also the search history are modeled, so that long-term feedback can be taken into account. Sample Density Balancing （SDB） is proposed under the framework to alleviate the drift phenomenon in EDA. A selection scheme based on Pareto ranking considering both the fitness and the historical sample density is adopted, which prevents the algorithm from repeatedly sampling in a small region and directs it to explore potentially optimal regions, thus helps it avoid being stuck into local optima. An MBOA （mixed Bayesian optimization algorithm） version of the framework is implemented and tested on several benchmark problems. Experimental results show that the proposed method outperforms a standard niching method in these benchmark problems.

关键词： evolutionary computation estimation of distribution algorithm Boltzmann selection

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Experimental comparisons with respect to the usage of the promising relations in EDA-based causal discovery

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ANNALS OF OPERATIONS RESEARCH 2018年第2期265卷 241-255页

作者： Ko, Song Lim, Hyunki Ko, Hoon Kim, Dae-Won Chung Ang Univ Sch Comp Sci & Engn Seoul 156756 South Korea Sungkyunkwan Univ Dept Comp Sci & Engn Seobu Ro 2066 Suwon South Korea

A Bayesian network is a promising probabilistic model to represent causal relations between nodes (random variables). One of the major research issue in a Bayesian network is how to infer causal relations from a dataset by constructing better heuristic learning algorithms. Many kinds of approaches were so far introduced, and estimation of distribution algorithms (EDAs) are one of the promising causal discovery algorithms. However, the performance of EDAs is considerably dependent on the quality of the first population because new individuals are reproduced from the previous populations. In this paper, we introduce a new initialization method for EDAs that extracts promising candidate causal relations based on causal scores. Then, we used the promising relations to construct a better first population and to reproduce better individuals until the learning algorithm is terminated. Experimental results show that EDAs infer a more number of correct causal relations when promising relations were used in EDA based structure learning. It means that the performance of EDAs can be improved by providing better local search space, and it was the promising relations in this paper.

关键词： Bayesian networks estimation of distribution algorithm Causal discovery Efficient learning Promising relation

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A novel approach based on preference-based index for interval bilevel linear programming problem

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JOURNAL OF INEQUALITIES AND APPLICATIONS 2017年第1期2017卷 1页

作者： Ren, Aihong Wang, Yuping Xue, Xingsi Baoji Univ Arts & Sci Dept Math Baoji 721013 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Coll Informat Sci & Engn Fuzhou 350118 Peoples R China

This paper proposes a new methodology for solving the interval bilevel linear programming problem in which all coefficients of both objective functions and constraints are considered as interval numbers. In order to keep as much uncertainty of the original constraint region as possible, the original problem is first converted into an interval bilevel programming problem with interval coefficients in both objective functions only through normal variation of interval number and chance-constrained programming. With the consideration of different preferences of different decision makers, the concept of the preference level that the interval objective function is preferred to a target interval is defined based on the preference-based index. Then a preference-based deterministic bilevel programming problem is constructed in terms of the preference level and the order relation <=(mw). Furthermore, the concept of a preference delta-optimal solution is given. Subsequently, the constructed deterministic nonlinear bilevel problem is solved with the help of estimation of distribution algorithm. Finally, several numerical examples are provided to demonstrate the effectiveness of the proposed approach.

关键词： bilevel programming interval number preference-based index estimation of distribution algorithm

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Finding equitable risk routes for hazmat shipments

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2019年第5期10卷 476-487页

作者： Chai, Huo He, Ruichun Ma, Changxi Dai, Cunjie Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Gansu Peoples R China

This paper develops a model to analyse hazmat shipments routing in the context of hazmat transportation between the specified origin-destination (OD) pair. A novel aspect of this model is the consideration of risk equity using the standard deviation, an established computation to assess equity. To solve the model, a two-phase method is developed, in which the multi-objective shortest path algorithm is used to obtain the Pareto-optimal paths set, and get the routes using estimation of distribution algorithm after paths choice. We then present a test problem of hazmat shipment with consideration of risk equity and discuss computational results.

关键词： hazmat shipments vehicle routing problem risk equity transportation network multi-objective shortest path estimation of distribution algorithm

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Feature subset selection by Bayesian networks:: a comparison with genetic and sequential algorithms

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2001年第2期27卷 143-164页

作者： Inza, I Larrañaga, P Sierra, B Univ Basque Country Dept Comp Sci & Artificial Intelligence E-20080 Donostia San Sebastian Basque Country Spain

In this paper we perform a comparison among FSS-EBNA, a randomized, population-based and evolutionary algorithm, and two genetic and other two sequential search approaches in the well-known feature subset selection (FSS) problem. In FSS-EBNA, the FSS problem, stated as a search problem, uses the estimation of Bayesian network algorithm (EBNA) search engine, an algorithm within the estimation of distribution algorithm (EDA) approach. The EDA paradigm is born from the roots of the genetic algorithm (GA) community in order to explicitly discover the relationships among the features of the problem and not disrupt them by genetic recombination operators. The EDA paradigm avoids the use of recombination operators and it guarantees the evolution of the population of solutions and the discovery of these relationships by the factorization of the probability distribution of best individuals in each generation of the search. In EBNA, this factorization is carried out by a Bayesian network induced by a cheap local search mechanism. FSS-EBNA can be seen as a hybrid Soft Computing system, a synergistic combination of probabilistic and evolutionary computing to solve the FSS task. Promising results on a set of real Data Mining domains are achieved by FSS-EBNA in the comparison respect to well-known genetic and sequential search algorithms. (C) 2001 Elsevier Science Inc. All rights reserved.

关键词： feature subset selection estimation of distribution algorithm soft computing estimation of Bayesian network algorithm Bayesian network predictive accuracy

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A parallel refined probabilistic approach for QoS-aware service composition

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 98卷 609-626页

作者： Wang, Hongbing Peng, Shunshun Yu, Qi Southeast Univ Sch Comp Sci & Engn SIPAILOU 2 Nanjing 210096 Jiangsu Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat SIPAILOU 2 Nanjing 210096 Jiangsu Peoples R China Rochester Inst Tech Coll Comp & Informat Sci Rochester NY USA

Service composition integrates existing online services to provide a value-added service. With the rapid growth of web services with similar functionalities, Quality of Service (QoS) has emerged as an important quantitative criterion on non-functional aspects. The optimization of QoS-aware service composition, depending on different aggregated QoS attributes has attracted significant attention. The dynamic nature of QoS-aware service composition adds further challenges to the optimization problem. Most existing approaches ignore the diversity of solutions, which have the potential to provide alternative compositions when changes occur. A few works only partially explore the search space and do not consider the optimality of solutions and the computational cost concurrently. To address these issues, we propose a novel reactive approach, called MrEDA, which integrates the estimation of distribution algorithm (EDA), restricted boltzmann machine (RBM), and multi-agent technology. It constructs a refined probabilistic model to diversify alternative solutions and guide the search by adaptively capturing the promising information of a service composition. Meanwhile, multiple agents make use of a flexible parallelism with distinct explorations and adaptive sampling to improve the global optimization and speed up the optimization. The effectiveness and efficiency of our approach for adaptive service composition is validated through an extensive experimental evaluation. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Adaptive service composition estimation of distribution algorithm Restricted boltzmann machine Multi-agent technology

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Hybrid evolutionary approaches for the single machine order acceptance and scheduling problem

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APPLIED SOFT COMPUTING 2017年 52卷 725-747页

作者： Chaurasia, Sachchida Nand Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

This paper presents two hybrid metaheuristic approaches, viz. a hybrid steady-state genetic algorithm (SSGA) and a hybrid evolutionary algorithm with guided mutation (EA/G) for order acceptance and scheduling (OAS) problem in a single machine environment where orders are supposed to have release dates and sequence dependent setup times are incurred in switching from one order to next in the schedule. OAS problem is an NP-hard problem. We have compared our approaches with the state-of-the-art approaches reported in the literature. Computational results show the effectiveness of our approaches. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Steady-state genetic algorithm estimation of distribution algorithm Evolutionary algorithm Guided mutation Order acceptance and scheduling Single machine scheduling Sequence dependent setup time

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