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World Congress on Engineering and Computer Science, WCES 2017

作者： Mutingi, Michael Mbohwa, Charles Musiyarira, Harmony IAENG Hong Kong Hong Kong Peoples R China Namibia Univ Sci & Technol Fac Engn Windhoek Namibia Univ Johannesburg Fac Engn & Built Environm Dept Qual & Operat Management Johannesburg South Africa

ISBN: (纸本)9789881404756

grouping problems are an important class of computational problems where the objective is to group, cluster or partition members of a set into desired sub sets. The grouping genetic algorithm is an extension of genetic algorithm that is heavily modified to model the structure of grouping problems. Since its inception, grouping genetic algorithms has been applied to several types of grouping problems. This paper presents an exploratory and chronological review of the grouping genetic algorithm approach. First, a survey of articles on grouping genetic algorithm approaches and its applications is presented. Second, a chronological review and analysis of research activities on the grouping genetic algorithm approach is presented. Third and finally, future trends and further research prospects are visualized and outlined.

关键词： genetic algorithms grouping grouping genetic algorithms exploratory study chronological review

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Mining Group Stock Portfolio by Using grouping genetic algorithms

Mining Group Stock Portfolio by Using Grouping Genetic Algor...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Chen, Chun-Hao Lin, Cheng-Bon Chen, Chao-Chun Tamkang Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Cheng Kung Univ Inst Mfg Informat & Syst Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781479974924

In this paper, a grouping genetic algorithm based approach is proposed for dividing stocks into groups and mining a set of stock portfolios, namely group stock portfolio. Each chromosome consists of three parts. grouping and stock parts are used to indicate how to divide stocks into groups. Stock portfolio part is used to represent the purchased stocks and their purchased units. The fitness of each chromosome is evaluated by the group balance and the portfolio satisfaction. The group balance is utilized to make the groups represented by the chromosome have as similar number of stocks as possible. The portfolio satisfaction is used to evaluate the goodness of profits and satisfaction of investor's requests of all possible portfolio combinations that can generate from a chromosome. Experiments on a real data were also made to show the effectiveness of the proposed approach.

关键词： data mining grouping genetic algorithms genetic algorithms grouping problems stock portfolio optimization

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Optimization of one-dimensional Bin Packing Problem with island parallel grouping genetic algorithms

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COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期75卷 176-186页

作者： Dokeroglu, Tansel Cosar, Ahmet Middle E Tech Univ Dept Comp Engn TR-06800 Ankara Turkey

The well-known one-dimensional Bin Packing Problem (BPP) of whose variants arise in many real life situations is a challenging NP-Hard combinatorial optimization problem. Metaheuristics are widely used optimization tools to find (near-) optimal solutions for solving large problem instances of BPP in reasonable running times. With this study, we propose a set of robust and scalable hybrid parallel algorithms that take advantage of parallel computation techniques, evolutionary grouping genetic metaheuristics, and bin-oriented heuristics to obtain solutions for large scale one-dimensional BPP instances. A total number of 1318 benchmark problems are examined with the proposed algorithms and it is shown that optimal solutions for 88.5% of these instances can be obtained with practical optimization times while solving the rest of the problems with no more than one extra bin. When the results are compared with the existing state-of-the-art heuristics, the developed parallel hybrid grouping genetic algorithms can be considered as one of the best one-dimensional BPP algorithms in terms of computation time and solution quality. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： One-dimensional bin packing grouping genetic algorithms Parallel computation Hybrid-heuristics

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MULTI-OBJECTIVE grouping genetic ALGORITHM FOR THE JOINT ORDER BATCHING, BATCH ASSIGNMENT, AND SEQUENCING PROBLEM

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2022年第3期17卷 188-204页

作者： Alejandro Cano, Jose Cortes, Pablo Antonio Campo, Emiro Correa-Espinal, Alexander A. Univ Medellin Fac Econ & Adm Sci Medellin Colombia Univ Seville Escuela Tecn Super Ingn Seville Spain Politecn Colombiano Jaime Isaza Cadavid Medellin Colombia Univ Nacl Colombia Fac Minas Medellin Colombia

This article solves the order batching, batch assignment, and sequencing problem (JOBASP) given multiple objectives and heterogeneous picking vehicles in multi-parallel-aisle warehouse systems. A multi-objective grouping genetic algorithm (GGA) is developed to minimize total travel time and total tardiness by implementing an encoding scheme where a gene represents orders grouped in a batch and the assignment of the batch to a picking vehicle. Computer simulations show that the proposed algorithm performs 25.4% better than a first come, first served (FCFS) rule-based heuristic and 10.2% better than an earliest due date (EDD) rule-based heuristic. The proposed GGA provides significant savings of up to 46.8% and 28.4% on travel time and tardiness, respectively, for these benchmark heuristics. Therefore, this article introduces a GGA to solve the JOBASP with a reasonable computing time, making this approach interesting for warehouse operators using heterogeneous picking vehicles and addressing multiple objectives.

关键词： Order picking order batching batch assignment sequencing grouping genetic algorithms multi-objective problem joint order picking

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An intelligence approach for group stock portfolio optimization with a trading mechanism

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KNOWLEDGE AND INFORMATION SYSTEMS 2020年第1期62卷 287-316页

作者： Chen, Chun-Hao Lu, Cheng-Yu Lin, Cheng-Bon Tamkang Univ Dept Comp Sci Informat Engn Taipei Taiwan

Optimizing a stock portfolio from a given financial dataset is always a very attractive task, as various factors should be considered. Hence, many methods based on evolutionary algorithms have been developed in the past decades to deal with the portfolio optimization problem. To provide a more flexible stock portfolio, we propose an algorithm to optimize a group stock portfolio by using a grouping genetic algorithm. In accordance with the optimized group stock portfolio, many stock portfolios can be generated and provided to investors. Each chromosome in the genetic algorithm is composed of a grouping part, a stock part and a stock portfolio part. The grouping and stock parts are used to indicate how to divide stocks into groups. The stock portfolio part is used to represent how many stocks should be selected from groups to form a portfolio and what units should be purchased. Four fitness functions are designed to evaluate each individual. Each of them is composed of the group balance, the unit balance, the stock price balance and the portfolio satisfaction. genetic operations, including crossover, mutation and inversion, are then executed to obtain new offspring to find the best solution. Furthermore, the proposed approach with a trading mechanism is designed to get a more useful group stock portfolio. Experiments on 31 stocks in accordance with four scenarios were conducted to show the merits and effectiveness of the proposed approach.

关键词： grouping genetic algorithms genetic algorithms Group stock portfolio grouping problem Portfolio optimization

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An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia

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APPLIED ENERGY 2018年 209卷 79-94页

作者： Salcedo-Sanz, Sancho Deo, Ravinesh C. Cornejo-Bueno, Laura Camacho-Gomez, Carlos Ghimire, Sujan Univ Alcala Dept Signal Proc & Commun Madrid Spain Univ Southern Queensland Sch Agr Computat & Environm Sci Int Ctr Appl Climate Sci Inst Agr & Environm Springfield Campus Springfield Cent Qld 4300 Australia Univ Southern Queensland Inst Agr & Environm Sch Agr Computat & Environm Sci Springfield Campus Springfield Cent Qld 4300 Australia

This research paper aims to develop a hybrid neuro-evolutionary wrapper-based model for daily global solar radiation estimation in the solar-rich Sunshine State of Queensland, Australia. To design a robust hybrid modelling mechanism, the Interim-ERA European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis data are employed to train and cross-validate the estimation model that is formulated by an evolutionary -type algorithm: the Coral Reefs Optimization (CRO) integrated with an Extreme Learning Machine (ELM) model. The hybrid CRO-(ELM) algorithm is applied in two stages: first for the feature selection process guided by an ELM algorithm (a class of fast training neural network tool) as the model's fitness function to screen an optimal set of predictor variables and second, for the estimation of the solar radiation using the optimally screened variables by the final hybrid CRO-(ELM)-ELM system. To benchmark the performance of the hybrid CRO-ELM algorithm for this estimation problem we apply an alternative, yet a similar feature screening approach: the grouping genetic Algorithm encoded into the ELM-based model (GGA-(ELM) used as the predictor mechanism). After the feature selection process is performed by the CRO algorithm that extracts the data patterns for accurate estimation the alternative objective algorithm is applied (in this case the ELM again) to formulate the hybrid CRO-(ELM)-ELM modelling system. To provide a rigorous evaluation of the CRO-(ELM)ELM hybrid system, alternative estimation approaches are considered: the Multivariate Adaptive Regression Splines (MARS), Multiple Linear Regression (MLR) and the Support Vector Regression (SVR). The hybrid CRO(ELM)-ELM system is tested in a real problem where the results are evaluated by means of several statistical score metrics and diagnostic plots of the observed and the estimated daily global solar radiation in the testing phase. We show that the hybrid CRO-(ELM)-ELM model is able to yield promis

关键词： Solar radiation estimation Coral Reefs Optimization Extreme Learning Machines grouping genetic algorithms Hybrid modelling system

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An efficient and effective approach for mining a group stock portfolio using mapreduce

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INTELLIGENT DATA ANALYSIS 2017年第Sup1期21卷 S217-S232页

作者： Chen, Chun-Hao Chen, Chao-Chun Nojima, Yusuke Tamkang Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Cheng Kung Univ Tainan Dept Comp Sci & Informat Engn Inst Mfg Informat & Syst Tainan Taiwan Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Osaka Japan

Portfolio optimization is always an attractive topic for research. In our previous approach, we proposed a method for mining a group stock portfolio that used grouping genetic algorithms. The derived group stock portfolio represents stocks in the same group that may have similar properties;consequently, a variety of stock portfolios could be offered to investors. However, the evaluation process used by this previous approach is time consuming when the number of stocks or groups increases. To address this problem, the map-reduce technique is considered. Map-reduce is a well-known approach for speeding up the mining process. This paper proposes a map-reduce-based approach to mine groups of stock portfolios and speed up the evolution process while still achieving results as similar as possible to the previous approach. Here, a chromosome represents a mapper number, a group number, a stock part and a portfolio part. Utilizing the mapper number, the chromosomes in a population are divided into subsets and sent to respective mappers, while the reducers execute fitness evaluation and genetic operations. The evolution process is repeated until the terminal conditions are reached. Finally, experiments on a real dataset are conducted to demonstrate the efficiency of the proposed approach.

关键词： grouping genetic algorithms grouping problems group stock portfolio map-reduce stock portfolio optimization

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Diverse Group Stock Portfolio Optimization Based on Investor Sentiment Index

Diverse Group Stock Portfolio Optimization Based on Investor...

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Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Chen, Chun-Hao Hong, Tzung-pei Chu, Shih-Chi Tamkang Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung Taiwan

ISBN: (纸本)9781538642030

Financial data analysis has always been an interesting issue and stock portfolio optimization is one of its most popular research topics. Recently, research on investor sentiment is also very popular in financial data analysis, and some scholars claim that it has impact on the stock market. In this paper, the proposed approach thus uses two investor sentiment indices and utilizes the grouping genetic algorithm to obtain a diverse group stock portfolio. Based on the fitness function defined in the previous approach, the adopted fitness function adds a new factor called risk of investor sentiment to evaluate the chromosomes for finding an appropriate diverse group stock portfolio. At last, experiments were conducted on a real stock dataset to verify the effectiveness of the proposed approach.

关键词： investor sentiment index trading strategy grouping genetic algorithms group stock portfolio maximally diverse grouping problem

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Life Cycle Design through Modularity and Disassembly

Life Cycle Design through Modularity and Disassembly

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International Symposium on Computer, Consumer and Control

作者： Hwai-En Tseng Shu-Hsuan Chang Dept. of Ind. Eng. & Manage. Nat. Chin-Yi Univ. of Technol. Taichung Taiwan Dept. of Ind. Educ. & Technol. Nat. Changhua Univ. of Educ. Changhua Taiwan

ISBN: (纸本)9781509030729

The fierce market competition result in the increasingly shorter product life cycles. It is a commonly known as a trend of green life cycle engineering design to consider the lightest harm to the environment in the early design stage. In this study, the authors attempted to establish an integrated assessment method for modular design and disassembly plan. In this study, the lamp and printer are used as examples to verify the method proposed in this study.

关键词： Design for green life cycle grouping genetic algorithms Liaison intensity Modularity Disassembly planning

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Course timetabling using evolutionary operators

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APPLIED SOFT COMPUTING 2013年第5期13卷 2504-2514页

作者： Qaurooni, Danial Akbarzadeh-T, Mohammad-R. Amirkabir Univ Technol Dept Comp Sci Tehran Iran Ferdowsi Univ Mashhad Ctr Excellence Soft Comp & Intelligent Informat P Mashhad Iran

Timetabling is the problem of scheduling a set of events while satisfying various constraints. In this paper, we develop and study the performance of an evolutionary algorithm, designed to solve a specific variant of the timetabling problem. Our aim here is twofold: to develop a competitive algorithm, but more importantly, to investigate the applicability of evolutionary operators to timetabling. To this end, the introduced algorithm is tested using a benchmark set. Comparison with other algorithms shows that it achieves better results in some, but not all instances, signifying strong and weak points. To further the study, more comprehensive tests are performed in connection with another evolutionary algorithm that uses strictly group-based operators. Our analysis of the empirical results leads us to question single-level selection, proposing, in its place, a multi-level alternative. (C) 2012 Elsevier B. V. All rights reserved.

关键词： Combinatorial optimization Scheduling and timetabling Evolutionary operators Memetic algorithms grouping genetic algorithms

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