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Latent Variable Model for estimation of distribution algorithm Based on a Probabilistic Context-Free Grammar

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2009年第4期13卷 858-878页

作者： Hasegawa, Yoshihiko Iba, Hitoshi Univ Tokyo Dept Computat Biol Grad Sch Frontier Sci Tokyo 2778561 Japan Univ Tokyo Grad Sch Engn Tokyo 1138656 Japan

estimation of distribution algorithms are evolutionary algorithms using probabilistic techniques instead of traditional genetic operators. Recently, the application of probabilistic techniques to program and function evolution has received increasing attention, and this approach promises to provide a strong alternative to the traditional genetic programming techniques. Although a probabilistic context-free grammar (PCFG) is a widely used model for probabilistic program evolution, a conventional PCFG is not suitable for estimating interactions among nodes because of the context freedom assumption. In this paper, we have proposed a new evolutionary algorithm named programming with annotated grammar estimation based on a PCFG with latent annotations, which allows this context freedom assumption to be weakened. By applying the proposed algorithm to several computational problems, it is demonstrated that our approach is markedly more effective at estimating building blocks than prior approaches.

关键词： EM algorithm estimation of distribution algorithm genetic programming probabilistic context-free grammar variational Bayes

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A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility

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APPLIED INTELLIGENCE 2018年第10期48卷 3707-3734页

作者： Perez-Rodriguez, Ricardo Hernandez-Aguirre, Arturo CONACYT CIMAT AC Bartolome Casas 314 Aguascalientes 20259 Ags Mexico CIMAT AC Jalisco S-N Guanajuato 36240 Gto Mexico

The flexible job-shop environments have become increasingly significant because of rapid improvements on shop floors such as production technologies, manufacturing processes and systems. Several real manufacturing and service companies have had to use alternative machines or processes for each operation and the availability of alternative process plans for each job in order to achieve good performance on the shop floor where conflicting objectives are common, e.g. the overall completion time for all jobs and the workload of the most loaded machine. In this paper, we propose a Pareto approach based on the hybridization of an estimation of distribution algorithm and the Mallows distribution in order to build better sequences for flexible job-shop scheduling problems with process plan flexibility and to solve conflicting objectives. This hybrid approach exploits the Pareto-front information used as an input parameter in the Mallows distribution. Various instances and numerical experiments are presented to illustrate that shop floor performance can be noticeably improved using the proposed approach. In addition, statistical tests are executed to validate this novel research.

关键词： Multiobjective optimization Evolutionary optimization estimation of distribution algorithm Flexible job-shop scheduling Mallows distribution Routing flexibility Process plan flexibility

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A hybrid estimation of distribution algorithm for simulation-based scheduling in a stochastic permutation flowshop

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COMPUTERS & INDUSTRIAL ENGINEERING 2015年 90卷 186-196页

作者： Wang, K. Choi, S. H. Lu, H. Wuhan Univ Econ & Management Sch Dept Management Sci & Engn Wuhan 430072 Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Hong Kong Peoples R China Wuhan Univ Technol Sch Logist Engn Dept Logist Management Wuhan 430070 Peoples R China

The permutation flowshop scheduling problem (PFSP) is NP-complete and tends to be more complicated when considering stochastic uncertainties in the real-world manufacturing environments. In this paper, a two-stage simulation-based hybrid estimation of distribution algorithm (TSSB-HEDA) is presented to schedule the permutation flowshop under stochastic processing times. To deal with processing time uncertainty, TSSB-HEDA evaluates candidate solutions using a novel two-stage simulation model (TSSM). This model first adopts the regression-based meta-modelling technique to determine a number of promising candidate solutions with less computation cost, and then uses a more accurate but time-consuming simulator to evaluate the performance of these selected ones. In addition, to avoid getting trapped into premature convergence, TSSB-HEDA employs both the probabilistic model of EDA and genetic operators of genetic algorithm (GA) to generate the offspring individuals. Enlightened by the weight training process of neural networks, a self-adaptive learning mechanism (SALM) is employed to dynamically adjust the ratio of offspring individuals generated by the probabilistic model. Computational experiments on Taillard's benchmarks show that TSSB-HEDA is competitive in terms of both solution quality and computational performance. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Permutation flowshop scheduling Stochastic processing times estimation of distribution algorithm Genetic algorithm Meta-model

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Using estimation of distribution algorithm for procedural content generation in video games

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2022年第4期23卷 495-533页

作者： Karkaj, Arash Moradi Lotfi, Shahriar Univ Coll Nabi Akram Dept Comp Engn Tabriz Iran Univ Tabriz Fac Math Stat & Comp Sci Dept Comp Sci Tabriz Iran

Content generation is one of the major challenges in the modern age. The video game industry is no exception and the ever-increasing demand for bigger titles containing vast volumes of content has become one of the vital challenges for the content generation domain. Conventional game development as a human product is not cost efficient and the need for more intelligent, advanced and procedural methods is evident in this field. In a sense, procedural content generation (PCG) is a Non-deterministic Polynomial-Hard optimization problem in which specific metrics should be optimized. In this paper, we use the estimation of distribution algorithm (EDA) to optimize the task of PCG in digital video games. EDA is an evolutionary stochastic optimization method and the introduction of probabilistic modeling as one of the main features of EDA into this problem domain is a reliable way to mathematically apply human knowledge to the challenging field of content generation. Acceptable performance of the proposed method is reflected in the results, which can inform the academia of PCG and contribute to the game industry.

关键词： Computer games Procedural content generation estimation of distribution algorithm Univariate marginal distribution algorithm and probabilistic modeling

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Enhancing the scalability of multi-objective optimization via restricted Boltzmann machine-based estimation of distribution algorithm

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INFORMATION SCIENCES 2013年 248卷 191-213页

作者： Shim, Vui Ann Tan, Kay Chen Cheong, Chun Yew Chia, Jun Yong Inst Infocomm Res Singapore 138632 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore Inst High Performance Comp Singapore 138632 Singapore

The exploitation of probability distribution of the solution set and linkage information among decision variables in guiding the search towards optimality is the main characteristic of estimation of distribution algorithms (EDAs). In this paper, the restricted Boltzmann machine (RBM) is modeled as a novel EDA in the context of multi-objective optimization. RBM is an energy-based stochastic neural network. The probabilities of the joint configuration over the visible and hidden units in the network are trained using contrastive divergence until the distribution over the global state reaches a certain level of thermal equilibrium. Subsequently, the probabilistic model is constructed using the energy function of the network. In addition, clustering in the phenotypic space is incorporated into the proposed algorithm. The effects on clustering and the stability of the trained network on optimization performance are rigorously examined. Experimental studies are conducted to analyze the performance of the proposed algorithm in scalable problems with large number of objective functions and decision variables. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Evolutionary computation estimation of distribution algorithm Multi-objective optimization Restricted Boltzmann machine Scalable problem

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DPM-IEDA: Dual probabilistic Model Assisted Interactive estimation of distribution algorithm for Personalized Search

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IEEE ACCESS 2019年 7卷 41006-41016页

作者： Chen, Yang Sun, Xiaoyan Gong, Dunwei Yao, Xiangjuan China Univ Min & Technol Sch Informat & Control Engn Control Theory & Control Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China China Univ Min & Technol Sch Sci Xuzhou 221116 Jiangsu Peoples R China

An interactive evolutionary algorithm (IEA) is powerful for solving personalized search when the user's preference can be well caught, expressed, and applied in the process of searching. Hybrid recommendation by articulating the content-based and collaborative filtering techniques is popular and effective for the personalized recommendation, but has not been developed to improve the performance of IEA for fulfilling the personalized search. Accordingly, we here propose an enhanced interactive estimation of distribution algorithm by designing dual-probabilistic models based on the hybrid recommendation for personalized search. The concept of hybrid personalized search is first defined from the viewpoint of using not only the historical search information but also the social or group preference. A dual-probabilistic model by sufficiently combining the content-based and collaborative filtering is presented and used to design the effective interactive estimation of distribution algorithm (IEDA). The probabilistic model is directly combined with the initialization of IEDA for illuminating the sparsity of the traditional IEA in encoding. The effectiveness of the proposed algorithm in fast and efficient searching with a lower computational cost is experimentally illustrated by two typical personalized searches on movies and TV series described with documents.

关键词： Collaborative filtering estimation of distribution algorithm interactive evolutionary computation personalized search

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A knowledge-guided estimation of distribution algorithm for energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 88卷

作者： Wu, Chu-ge Zhang, Ruochen Xia, Yuanqing Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

The assembly line serves as a fundamental system in discrete production. To address the challenges in balancing robotic assembly lines, timely part feeding, and the need for sustainable manufacturing, this paper studies an energy -efficient Joint Robotic Assembly Line Balancing and Feeding Problem (JRALB-FP) with the criteria of minimizing both cycle time and total fuel consumption cost. Considering the complexity of the multi -problem and multi -objective optimization, a knowledge -guided estimation of distribution algorithm (KEDA) is proposed to solve energy -efficient JRALB-FP. First, a probability model of EDA for task -workstation allocation paired with a heuristic method -based sampling mechanism is created. Using this probability model, a specific encoding mechanism is designed for part -trailer allocation, and good initial solutions are produced. Second, several properties of the bi-objective problem are analyzed to guide the design of local search operators for both objectives optimization. Third, the updating mechanism of the probability model is designed to learn from the elite solutions. Fourth, two knowledge -guided local search operators are designed and implemented to exploit better non -dominated solutions sufficiently. A design of experiment is carried out to determine the parameters. Extensive computational tests and comparisons with the state-of-the-art multi -objective algorithms are carried out, which verify the effectiveness of the knowledge -guided local search operators, the problem -oriented heuristic -based sampling mechanism, and the special designs of the KEDA in solving the energy -efficient JRALB-FP.

关键词： Robotic assembly line balancing Part feeding estimation of distribution algorithm Production Optimization

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Reference line-based estimation of distribution algorithm for many-objective optimization

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KNOWLEDGE-BASED SYSTEMS 2017年 132卷 129-143页

作者： Sun, Yanan Yen, Gary G. Yi, Zhang Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Multi-Objective Evolutionary algorithms (MOEAs) are preferred in solving multi-objective optimization problems due to their considerable performance giving decision-maker a set of not only convergent but diversified promising solutions. However, the scalability of MOEAs deteriorates in addressing many objective optimization problems which involve more than three conflicting objectives. The principal reason is largely due to the deficiency of the existing genetic operators which cannot generate promising offspring from parents chosen by the Pareto-dominance rule in these MOEAs. estimation of distribution algorithms (EDAs) generate offspring with a probabilistic model built from the statistics extracting upon existing solutions to expectedly alleviate the weakness arisen in genetic operators. In this paper, a reference line-based EDA is proposed for effectively solving many-objective optimization problems. Specifically, the estimation model is built based on the reference lines in the decision space to sample solutions with favorable proximity. Then solutions with considerable diversity in Pareto-optimal front are selected. These two phases collectively promote the needed convergence and diversity for the proposed algorithm. To evaluate the performance, extensive experiments are performed against four state-of-the-art many objective evolutionary algorithms and two EDAs over DTLZ and WFG test suites with 5-, 8-, 10-, and 15-objective. Experimental results quantified by the selected performance metrics indicate that the proposed algorithm shows significant competitiveness in tackling many-objective optimization problems. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Many-objective evolutionary algorithm Evolutionary computation Reference line estimation of distribution algorithm

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An enhanced estimation of distribution algorithm for solving hybrid flow-shop scheduling problem with identical parallel machines

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第9-12期68卷 2043-2056页

作者： Wang, Sheng-yao Wang, Ling Liu, Min Xu, Ye Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

In this paper, an enhanced estimation of distribution algorithm (EEDA) is proposed to solve the hybrid flow-shop scheduling problem with identical parallel machines to minimize makespan. To evaluate the individuals, some decoding rules including the improved permutation scheduling rule, the improved list scheduling rule and the backward scheduling rule are designed for the permutation-based encoding scheme, and then a hybrid decoding method is proposed. To describe the distribution of the solution space for the EEDA, a probability model is built and used to generate new individuals by sampling. To well trace the region with promising solutions, a mechanism is provided to update the model with the superior sub-population. To enhance the exploitation capability, multiple local search operators are incorporated in the framework of the EEDA. The influence of the parameter setting is investigated based on the Taguchi method of design-of-experiment. Extensive numerical testing results based on sets of the well-known benchmarks and the comparisons with some existing algorithms demonstrate the effectiveness of the proposed algorithm.

关键词： Hybrid flow-shop scheduling Identical parallel machine Decoding method estimation of distribution algorithm Probability model

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Solving system-level synthesis problem by a multi-objective estimation of distribution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第5期41卷 2496-2513页

作者： Wang, Ling Fang, Chen Suganthan, Ponnuthurai Nagaratnam Liu, Min Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this paper, the system-level synthesis problem (SLSP) is modeled as a multi-objective mode-identity resource-constrained project scheduling problem with makespan and resource investment criteria (MOMIRCPSP-MS-RI). Then, a hybrid Pareto-archived estimation of distribution algorithm (HPAEDA) is presented to solve the MOMIRCPSP-MS-RI. To be specific, the individual of the population is encoded as the activity-mode-priority-resource list (AMPRL), and a hybrid probability model is used to predict the most promising search area, and a Pareto archive is used to preserve the non-dominated solutions that have been explored, and another archive is used to preserve the solutions for updating the probability model. Moreover, specific sampling mechanism and updating mechanism for the probability model are both provided to track the most promising search area via the EDA-based evolutionary search. Finally, the modeling methodology and the HPAEDA are tested by an example of a video codec based on the H.261 image compression standard. Simulation results and comparisons demonstrate the effectiveness of the modeling methodology and the proposed algorithm. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： estimation of distribution algorithm Probability model System-level synthesis problem Project scheduling Image compression standard

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