Genetic algorithm for item selection with cross-selling considerations

This paper presents a robust hybrid genetic algorithm for optimization of space structures using the augmented Lagrangian method. An attractive characteristic of genetic algorithm is that there is no line search and the problem of computation of derivatives of the objective function and constraints is avoided. This feature of genetic algorithms is maintained in the hybrid genetic algorithm presented in this paper. Compared with the penalty function‐based genetic algorithm, only a few additional simple function evaluations are needed in the new algorithm. Furthermore, the trial and error approach for the starting penalty function coefficient and the process of arbitrary adjustments are avoided. There is no need to perform extensive numerical experiments to find a suitable value for the penalty function coefficient for each type or class of optimization problem. The algorithm is general and can be applied to a broad class of optimization problems.

Weighted Frequent Itemset (WFI) mining is an important model in data mining. The popular adoption and successful industrial application of this model has been hindered by the following two obstacles: (i) finding WFIs is a computationally expensiveness process as these itemsets do not satisfy the downward closure property and (ii) lack of parallel algorithms to find WFIs in very large databases (e.g. astronomical data and twitter data). This paper makes an effort to address these two obstacles. Two pattern-growth algorithms, Sequential Weighted Frequent Pattern-growth and Parallel Weighted Frequent Pattern-growth, have been introduced to discover WFIs efficiently. Both algorithms employ three novel pruning techniques to reduce the computational cost effectively. The first pruning technique prunes some of the uninteresting items by employing a criterion known as cutoff weight. The second pruning technique, called conditional pattern base elimination, eliminates the construction of conditional pattern bases if a suffix item is an uninteresting item. The third pruning technique, called pattern-growth termination, defines a new terminating condition for the pattern-growth technique. Experimental results demonstrate that the proposed algorithms are memory and runtime efficient, and highly scalable as well.

Background: Xiaochaihu capsule is composed of seven traditional Chinese medicines. The pharmacopoeia only focuses on the quantitative detection of baicalin, which cannot fully reflect the quality of the preparation. Some medium polar components were used to establish the fingerprint of Xiaochaihu capsule, but there was no report on the strong polar components. Methods: A high performance liquid chromatography-corona charged aerosol detection technology was used to establish a fingerprint analysis method for Xiaochaihu capsules following an analytical quality by design approach. Definitive screening designed experiments were used to optimize the method parameters. A stepwise regression method was used to build quantitative models. The method operable design region was calculated using the experimental error simulation method. Plackett–Burman designed experiments were carried out to test robustness. Results: The contents of four components were simultaneously determined. There were seven common peaks in the fingerprint. The common peak area accounted for 91.72%. Both fingerprint and quantitative analysis methods were validated as applicable in the methodology study. The quantitative fingerprint analysis method for sugar components can fill the gap in the detection of strong polar components in the existing methods. It provides a new technology for the comprehensive overall evaluation of Xiaochaihu capsule.

Despite the wide range of applications in different areas, the world-urging factors like Internet Search Speed or the rate of data retrieval, has found its wherewithal from the concept of Evolutionary Algorithms. Due to the suitability of the nature of the algorithms, query optimization for web mining, has been implemented in terms of Genetic Algorithms. This paper shall review upon the characteristics of Genetic Algorithms, suitability to web search optimization, their impact on implementation aspects, challenges, success rate and suggested enhancements in further research.

In engineering projects, the coordination and management of multiple objectives is an important part of project management, which has a direct impact on the realization of objectives in terms of project duration, cost, and quality. The current engineering project investment presents the characteristics of large scale, long period, and many risks, which puts forward higher requirements for the multiobjective coordinated management of the project. Therefore, the analysis and optimization of the various objectives of the engineering project is the basis for achieving multiobjective balance and coordination. The comprehensive consideration of the risks faced by the engineering project and the dynamics of the environment in which it is located can make the objective optimization more realistic. On the basis of risk identification and risk evaluation, this paper is committed to considering multiple objective factors that affect the outcome of risk decision-making to achieve the optimization of decision-making schemes and applies multiobjective genetic algorithm to the optimization of decision-making schemes, thus finding a way to optimize the decision-making scheme. This paper analyzes the research progress and status of construction project risk management decision and multiobjective evolutionary algorithm and points out the imperfections of the current research. Then it introduces the related theory of construction project risk management decision, the related terminology of multiobjective optimization problem and the method used in this paper —the principle, process, and characteristics of genetic algorithm and prepares for the following problem solving. In this paper, a mathematical model for multiobjective decision-making of engineering project risk management is established, and the multiobjective genetic algorithm is used to solve the model. Through the analysis of examples, a series of Pareto optimal solutions with good convergence and diversity are obtained, the best combination of various risk control measures is found, and three goals of risk evaluation value, management cost, and risk loss are achieved. The two models with or without risk correlation present certain differences, the model with correlation considered is more accurate and applicable than the model without correlation considered in the existing research.

Intelligent Machine Evolutionary Algorithm Learning Based on Artificial Intelligence

A self-organizing migrating genetic algorithm for constrained optimization

Dynamic embedding for genetic VLSI circuit partitioning

Prediction of inherent viscosity for polymers containing natural amino acids from the theoretical derived molecular descriptors

Genetic algorithms are optimization techniques especially useful in functions whose nonlinearity makes an analytical optimization impossible. This kind of functions appear when using least squares estimators in nonlinear regression problems. Least squares optimizers in general, and the Levenberg-Marquardt method in particular, are iterative methods especially designed to solve this kind of problems, but the results depend on both the features of the problem and the closeness to the optimum of the starting point. In this paper we study the least squares estimator and the optimization methods that are based on it. Then we analyze those features of real-coded genetic algorithms that can be useful in the context of nonlinear regression. Special attention will be devoted to the crossover operator, and a new operator based on confidence intervals will be proposed. This crossover provides an equilibrium between exploration and exploitation of the search space, which is very adequate for this kind of problems. To analyze the fitness and robustness of the proposed crossover operator, we will use three complex nonlinear regression problems with search domains of different amplitudes and compare its performance with that of other crossover operators and with the Levenberg-Marquardt method using a multi-start scheme.KeywordsReal coded genetic algorithmsNonlinear regressionConfidence interval based crossover.

Differential Evolution (DE) has recently emerged as simple and efficient algorithm for global optimization over continuous spaces.DE shares many features of the classical Genetic Algorithms (GA). But it is much easier to implement than GA and applies a kind of differential mutation operator on parent chromosomes to generate the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world, resulting in a lot of variants of the basic algorithm, with improved performance. This chapter begins with a conceptual outline of classical DE and then presents several significant variants of the algorithm in greater details.KeywordsDifferential EvolutionDifferential Evolution AlgorithmTarget VectorTrial VectorDonor VectorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Since January 2015 the government has begun to abolish some product subsidies, especially for the marketing area of Java Bali and Madura. This opens up opportunities for new players to enter the oil and gas sector given the increasingly promising profits. the emergence of this new competitor adds to fierce competition between outlets, and to maintain cost efficiency a flexibility strategy and innovation strategy are implemented, especially related to the distribution of products from the company to all outlets where transportation equipment is adjusted so that it can transport various types of products to several outlets at a time . This condition is accompanied by a policy that a conveyance may send to various outlets at a time. Initially, business performance improved rapidly but with the increasing number of outlets there were several product distribution accidents, subsidized deposits, customers switching to other outlets including those owned by foreign companies, and backlog transaction constraints. The problem is that the partner's trust has declined including customers to the company as a result of very strict supervision in the midst of environmental changes as a result of changes in government policies and regulations lately. The sample became the object of research is customer stations in Central Java and Yogyakarta as many as 200 customer in the Gas Station Central Java and Yogyakarta. Methods of data analysis used in this study is a quantitative analysis method where quantitative data analysis method is a method of analysis in the form of figures and calculations using statistical methods. In simplify the analysis process, will be used SEM, AMOS.
 Keywords : Product Cohesiveness, Quality Business Strategy, Business Performance, Competitive Advantage

This paper presents an efficient hybrid feature selection model based on Support Vector Machine (SVM) and Genetic Algorithm (GA) for large healthcare databases. Even though SVM and GA are robust computational paradigms, the combined iterative nature of a SVM-GA hybrid system makes runtime costs infeasible when using large databases. This paper utilizes hierarchical clustering to reduce dataset size and SVM training time, multi-resolution parameter search for efficient SVM model selection, and chromosome caching to avoid redundant fitness evaluations. This approach significantly reduces runtime and improves classification performance.

With the development of network information technology, the Internet has become an important means of information dissemination, and the "Internet +" model has become a new channel for information dissemination in the new era. This mode of communication has penetrated into all aspects of social life, and has also brought new opportunities for the dissemination of sports culture. For this reason, the dissemination of sports culture should actively adapt to this network communication method, form a "Internet + sports culture" sharing model, enhance the influence and radiation scope of sports culture communication, and then enhance the competitiveness of sports culture communication. This article first analyzes the current research status of cultural communication at home and abroad, and finds that the current cultural communication mostly uses qualitative analysis methods, while quantitative analysis methods are less used. For this reason, it is proposed to use neural network algorithm to carry out cultural fusion propagation research. Aiming at the complicated operation of current neural network algorithm, a neural network algorithm based on genetic algorithm is constructed. Based on this, the article discusses the sports culture communication strategy under the background of the Internet, in order to have a positive impact on the communication of sports culture.

Machine learning has become an integral part of our life in today's world. Machine learning when applied to real-world applications suffers from the problem of high dimensional data. Data can have unnecessary and redundant features. These unnecessary features affect the performance of classification systems used in prediction. Selection of important features is the first step in developing any decision support system. In this paper, the authors have proposed a hybrid feature selection method GARFE by integrating GA (genetic algorithm) and RFE (recursive feature elimination) algorithms. Efficiency of proposed method is analyzed using support vector machine classifier on the scale of accuracy, sensitivity, specificity, precision, F-measure, and execution time parameters. Proposed GARFE method is also compared to eight other feature selection methods. Results demonstrate that the proposed GARFE method has increased the performance of classification systems by removing irrelevant and redundant features.