The location affects the competitiveness and market share of a new entry enterprise, especially for a retail enterprise. This study focuses on the competitive location of new chain stores. In this paper, a bi-level model is proposed to formulate the competitive location problem. And the model also considers the pricing game between the new entry enterprise and the existing competitor. The model optimizes the location by maximizing the benefit on the principle of the Nash equilibrium. A heuristic algorithm is proposed to solve the model. Results show the feasibility of the proposed model and provide managerial insights for decision makers to determine an appropriate location.
In machine learning, discretization and feature selection (FS) are important techniques for preprocessing data to improve the performance of an algorithm on high-dimensional data. Since many FS methods require discrete data, a common practice is to apply discretization before FS. In addition, for the sake of efficiency, features are usually discretized individually (or univariate). This scheme works based on the assumption that each feature independently influences the task, which may not hold in cases where feature interactions exist. Therefore, univariate discretization may degrade the performance of the FS stage since information showing feature interactions may be lost during the discretization process. Initial results of our previous proposed method [evolve particle swarm optimization (EPSO)] showed that combining discretization and FS in a single stage using bare-bones particle swarm optimization (BBPSO) can lead to a better performance than applying them in two separate stages. In this paper, we propose a new method called potential particle swarm optimization (PPSO) which employs a new representation that can reduce the search space of the problem and a new fitness function to better evaluate candidate solutions to guide the search. The results on ten high-dimensional datasets show that PPSO select less than 5% of the number of features for all datasets. Compared with the two-stage approach which uses BBPSO for FS on the discretized data, PPSO achieves significantly higher accuracy on seven datasets. In addition, PPSO obtains better (or similar) classification performance than EPSO on eight datasets with a smaller number of selected features on six datasets. Furthermore, PPSO also outperforms the three compared (traditional) methods and performs similar to one method on most datasets in terms of both generalization ability and learning capacity.
A method of evolving an image descriptor comprises receiving a set of pixel values associated to an image or part thereof; applying at least two first-order statistical functions to the set of pixel values to obtain respective function outputs; and applying at least one arithmetic operator to a pair of function outputs to obtain at least one arithmetic output.
Cao Truong Tran
Zhang, Mengjie
Andreae, Peter
Xue, Bing
Lam Thu Bui
Many real-world datasets suffer from the unavoidable issue of missing values. Classification with incomplete data has to be carefully handled because inadequate treatment of missing values will cause large classification errors. Using imputation to transform incomplete data into complete data is a common approach to classification with incomplete data. However, simple imputation methods are often not accurate, and powerful imputation methods are usually computationally intensive. A recent approach to handling incomplete data constructs an ensemble of classifiers, each tailored to a known pattern of missing data. The main advantage of this approach is that it can classify new incomplete instances without requiring any imputation. This paper proposes an improvement on the ensemble approach by integrating imputation and genetic-based feature selection. The imputation creates higher quality training data. The feature selection reduces the number of missing patterns which increases the speed of classification, and greatly increases the fraction of new instances that can be classified by the ensemble. The results of experiments show that the proposed method is more accurate, and faster than previous common methods for classification with incomplete data.
Zhang, Mengjie
Zhang, Yi
Yang, Shuang
Zhou, Jian
Gao, Weiwu
Yang, Xia
Yang, Di
Tian, Zhiqiang
Wu, Yuzhang
Ni, Bing
The transcription factor Yin Yang 1 (YY1) is a multifunctional protein that can activate or repress gene expression, depending on the cellular context. While YY1 is ubiquitously expressed and highly conserved between species, its role varies among the diverse cell types and includes proliferation, differentiation, and apoptosis. Upregulated YY1 expression is found in pathogenic conditions, such as human hepatocellular carcinoma and hepatitis B virus infection, and its roles in the molecular pathogenic mechanisms in liver (i.e., fibrosis, carcinogenesis, viral-induced injury) are currently being elucidated. The most recent studies have revealed that YY1 is deeply involved in such dysregulated cellular metabolisms as glycometabolism, lipid metabolism, and bile acid metabolism, which are all involved in various diseases. In this review, we will summarize the current knowledge on YY1 in liver diseases, providing a focused discussion on the characterized and probable underlying mechanisms, as well as a reasoned evaluation of the potential for YY1-mediated pathology as drug targets in liver disease therapies.
Yu, Shaoqiang
Yu, Yang
Zhang, Mengjie
Wu, Enze
Wu, Lan
In recent years, the proportion of fuel costs in the total shipping costs grows up with the increase in international crude oil price. The rising cost seriously restricts the development of shipping enterprises. Many researches and cases show that reducing the ship's fuel consumption by cautiously and reasonably decelerating the ship is the most simple and effective mean to control the shipping cost. However, the transportation time is longer when the speed is reduced. In other words, shipper would have to pay more for per unit transportation time. When the increase in transit time exceeds a certain range, liner companies are likely to lose customers. This article aims to maximize the profit of a liner company, which consisted of the variety of fuel cost, fixed cost, operating cost, and other related factors. And at the same time, the change in the shipper's transport time cost and freight cost is considered. Then, a speed-freight optimization model based on the bat algorithm is established using MATLAB toolbox, in which the liner line is segmented. Finally, this article uses the model to optimize the route of the Far East to North America. The results show that the model can guarantee the quality of transport and reduce transport cost by adjusting speed at the same time.
Karunakaran, Deepak
Mei, Yi
Chen, Gang
Zhang, Mengjie
Job shop scheduling (JSS) is a hard problem with most of the research focused on scenarios with the assumption that the shop parameters such as processing times, due dates are constant. But in the real world uncertainty in such parameters is a major issue. In this work, we investigate a genetic programming based hyper-heuristic approach to evolving dispatching rules suitable for dynamic job shop scheduling under uncertainty. We consider uncertainty in processing times and consider multiple job types pertaining to different levels of uncertainty. In particular, we propose an approach to use exponential moving average of the deviations of the processing times in the dispatching rules. We test the performance of the proposed approach under different uncertain scenarios. Our results show that the proposed method performs significantly better for a wide range of uncertain scenarios.
Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.
Branke, Juergen
Su Nguyen
Pickardt, Christoph W.
Zhang, Mengjie
Hyper-heuristics have recently emerged as a powerful approach to automate the design of heuristics for a number of different problems. Production scheduling is a particularly popular application area for which a number of different hyper-heuristics have been developed and are shown to be effective, efficient, easy to implement, and reusable in different shop conditions. In particular, they seem to be a promising way to tackle highly dynamic and stochastic scheduling problems, an aspect that is specifically emphasized in this survey. Despite their success and the substantial number of papers in this area, there is currently no systematic discussion of the design choices and critical issues involved in the process of developing such approaches. This paper strives to fill this gap by summarizing the state-of-the-art approaches, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling. This paper also identifies challenges and open questions and highlights various directions for future work.