Journal articles: 'Knowledge-based genetic algorithm'

1

Aldridge, C. J., S. McKee, J. R. McDonald, S. J. Galloway, K. P. Dahal, M. E. Bradley, and J. F. Macqueen. "Knowledge-based genetic algorithm for unit commitment." IEE Proceedings - Generation, Transmission and Distribution 148, no. 2 (2001): 146. http://dx.doi.org/10.1049/ip-gtd:20010022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Purnomo, Muhammad Ridwan Andi, Chairul Saleh, Reny Lituhayu Lagaida, and Azmi Hassan. "Knowledge-Based Genetic Algorithm for Multidimensional Data Clustering." Applied Mechanics and Materials 606 (August 2014): 277–80. http://dx.doi.org/10.4028/www.scientific.net/amm.606.277.

Full text

Abstract:

In this paper, a new approach of genetic algorithm called knowledge-based Genetic Algorithm (KBGA-Clustering) is proposed for multidimensional data clustering . Basically, this method adopts knowledge of what called as appropriate cluster centre for a fixed number of k-cluster. The chromosome which has inappropriate genes will be penalised with maximum value to prohibit it in the next generation. The experimental result is also provided for KBGA-Clustering and Genetic Algorithm-Clustering (GA-Clustering) to present the performance. Based on the observation, KBGA-Clustering presents better performance and more optimum solution compared to conventional GA-Clustering.

APA, Harvard, Vancouver, ISO, and other styles

3

Prakash, A., Felix T. S. Chan, and S. G. Deshmukh. "FMS scheduling with knowledge based genetic algorithm approach." Expert Systems with Applications 38, no. 4 (April 2011): 3161–71. http://dx.doi.org/10.1016/j.eswa.2010.09.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Li, Chao, Xiaogeng Chu, Yingwu Chen, and Lining Xing. "A knowledge-based technique for initializing a genetic algorithm." Journal of Intelligent & Fuzzy Systems 31, no. 2 (July 22, 2016): 1145–52. http://dx.doi.org/10.3233/jifs-169043.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Kanoh, Hitoshi, and Yuusuke Sakamoto. "Knowledge-based genetic algorithm for university course timetabling problems." International Journal of Knowledge-based and Intelligent Engineering Systems 12, no. 4 (November 7, 2008): 283–94. http://dx.doi.org/10.3233/kes-2008-12403.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Alaoui, Abdiya, and Zakaria Elberrichi. "Neuronal Communication Genetic Algorithm-Based Inductive Learning." Journal of Information Technology Research 13, no. 2 (April 2020): 141–54. http://dx.doi.org/10.4018/jitr.2020040109.

Full text

Abstract:

The development of powerful learning strategies in the medical domain constitutes a real challenge. Machine learning algorithms are used to extract high-level knowledge from medical datasets. Rule-based machine learning algorithms are easily interpreted by humans. To build a robust rule-based algorithm, a new hybrid metaheuristic was proposed for the classification of medical datasets. The hybrid approach uses neural communication and genetic algorithm-based inductive learning to build a robust model for disease prediction. The resulting classification models are characterized by good predictive accuracy and relatively small size. The results on 16 well-known medical datasets from the UCI machine learning repository shows the efficiency of the proposed approach compared to other states-of-the-art approaches.

APA, Harvard, Vancouver, ISO, and other styles

7

Chen, Si Hua. "A Effective Knowledge Integration Algorithm Based on Culture Algorithm Framework." Applied Mechanics and Materials 26-28 (June 2010): 310–14. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.310.

Full text

Abstract:

The key to obtain an effective knowledge base to improve decision quality and enhance organizational core competency is integrating the knowledge from different subjects and sources. We put forward a knowledge integration strategy based on culture algorithm framework. It encodes the knowledge uniformly and evolutes among the two phases of population space and belief space. Through the communication protocol established among the two spaces, an effective and concise knowledge base is obtained. The experiment shows that comparing with traditional genetic algorithm the model can classify the knowledge more precisely, reduce redundant knowledge and remove contradictory knowledge.

APA, Harvard, Vancouver, ISO, and other styles

8

Zeng, Fu Hong, and Lan Hua Zhou. "BOM Configuration Based on Genetic Algorithm." Advanced Materials Research 156-157 (October 2010): 529–33. http://dx.doi.org/10.4028/www.scientific.net/amr.156-157.529.

Full text

Abstract:

In order to meet the requirements of product design and find suitable combination of within a short time, the concept of Knowledge-body based on BOM (KBOM) was put forward, which consisted of basic entities that could support all activities throughout product lifecycle with less redundancy, and all the product data throughout product lifecycle was associated with the core of KBOM directly or indirectly. A reusable and sharable configuration model of product was established to represent domain-specific knowledge based on KBOM. Genetic algorithm was used to solve the issue concerned with BOM configuration in a short time with value coefficient, quality level and reliability as evaluation parameters. Finally, as a case study, the display module of a notebook was analyzed. The results indicate that the evaluation can be effectively applied in a large-scale BOM configuration.

APA, Harvard, Vancouver, ISO, and other styles

9

Hua, Haiyan, and Shuwen Lin. "New knowledge-based genetic algorithm for excavator boom structural optimization." Chinese Journal of Mechanical Engineering 27, no. 2 (March 2014): 392–401. http://dx.doi.org/10.3901/cjme.2014.02.392.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Ware, J. M., I. D. Wilson, and J. A. Ware. "A knowledge based genetic algorithm approach to automating cartographic generalisation." Knowledge-Based Systems 16, no. 5-6 (July 2003): 295–303. http://dx.doi.org/10.1016/s0950-7051(03)00031-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Zhang, Faping, Li Li, and Cuixiang Zhou. "A Knowledge Context Fuzzy Clustering Method Based on Genetic Algorithm." MATEC Web of Conferences 139 (2017): 00064. http://dx.doi.org/10.1051/matecconf/201713900064.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Pan, Hao, Wen Jun Hou, and Tie Meng Li. "Genetic Algorithm for Assembly Sequences Planning Based on Heuristic Assembly Knowledge." Applied Mechanics and Materials 44-47 (December 2010): 3657–61. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3657.

Full text

Abstract:

To improve the efficiency of Assembly Sequences Planning (ASP), a new approach based on heuristic assembly knowledge and genetic algorithm was proposed. First, Connection Graph of Assembly (CGA) was introduced, and then, assembly knowledge was described in the form of Assembly Rings, on that basis, the assembly connection graph model containing Assembly Rings was defined, and the formation of initial population algorithm was given. In addition, a function was designed to measure the feasible assembly and then the genetic algorithm fitness function was given. Finally, an example was shown to illustrate the effectiveness of the algorithm.

APA, Harvard, Vancouver, ISO, and other styles

13

Babovic, Vladan. "Introducing knowledge into learning based on genetic programming." Journal of Hydroinformatics 11, no. 3-4 (July 1, 2009): 181–93. http://dx.doi.org/10.2166/hydro.2009.041.

Full text

Abstract:

This work examines various methods for creating empirical equations on the basis of data while taking advantage of knowledge about the problem domain. It is demonstrated that the use of high level concepts aid in evolving equations that are easier to interpret by domain specialists. The application of the approach to real-world problems reveals that the utilization of such concepts results in equations with performance equal or superior to that of human experts. Finally, it is argued that the algorithm is best used as a hypothesis generator assisting scientists in the discovery process.

APA, Harvard, Vancouver, ISO, and other styles

14

Olteanu, Marius, Nicolae Paraschiv, and Petia Koprinkova-Hristova. "Genetic Algorithms vs. Knowledge-Based Control of PHB Production." Cybernetics and Information Technologies 19, no. 2 (June 1, 2019): 104–16. http://dx.doi.org/10.2478/cait-2019-0018.

Full text

Abstract:

Abstract The paper proposes an approach using Genetic Algorithm (GA) for development of optimal time profiles of key control variable of Poly-HydroxyButyrate (PHB) production process. Previous work on modeling and simulation of PHB process showed that it is a highly nonlinear process that needs special controllers based on human experience, as such fuzzy logic controller proved to be a good choice. Fuzzy controllers are not totally replaced, due to the specific process knowledge that they contain. The achieved results are compared with previously proposed knowledge-based approach to the same optimal control task.

APA, Harvard, Vancouver, ISO, and other styles

15

BALASUBRAMANIAN, Vinoth Kumar, and Karpagam MANAVALAN. "Knowledge-based genetic algorithm approach to quantization table generation for the JPEG baseline algorithm." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 1615–35. http://dx.doi.org/10.3906/elk-1310-179.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Won, Chaehwan, Jinhwa Kim, and Jae Kwon Bae. "Using genetic algorithm based knowledge refinement model for dividend policy forecasting." Expert Systems with Applications 39, no. 18 (December 2012): 13472–79. http://dx.doi.org/10.1016/j.eswa.2012.06.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Johns, Matthew B., Edward Keedwell, and Dragan Savic. "Knowledge-based multi-objective genetic algorithms for the design of water distribution networks." Journal of Hydroinformatics 22, no. 2 (November 29, 2019): 402–22. http://dx.doi.org/10.2166/hydro.2019.106.

Full text

Abstract:

Abstract Water system design problems are complex and difficult to optimise. It has been demonstrated that involving engineering expertise is required to tackle real-world problems. This paper presents two engineering inspired hybrid evolutionary algorithms (EAs) for the multi-objective design of water distribution networks. The heuristics are developed from traditional design approaches of practicing engineers and integrated into the mutation operator of a multi-objective EA. The first engineering inspired heuristic is designed to identify hydraulic bottlenecks within the network and eliminate them with a view to speeding up the algorithm's search to the feasible solution space. The second heuristic is based on the notion that pipe diameters smoothly transition from large, at the source, to small at the extremities of the network. The performance of the engineering inspired hybrid EAs is compared with Non-Dominated Sorting Genetic Algorithm II and assessed on three networks of varying complexity, two benchmarks and one real-world network. The experiments presented in this paper demonstrate that the incorporation of engineering expertise can improve EA performance, often producing superior solutions both in terms of mathematical optimality and also engineering feasibility.

APA, Harvard, Vancouver, ISO, and other styles

18

Liang, Ting Ting. "Intelligent Subject and Knowledge Retrieval Algorithm Based on Ontology." Applied Mechanics and Materials 687-691 (November 2014): 1452–56. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1452.

Full text

Abstract:

For the problems of obscure demand of users, insufficiency and difficulty in correct acquisition in the process of knowledge retrieval, this paper combines with basic theory of interactive genetic algorithm to discuss its application model in the process of requirement and acquisition of knowledge retrieval. And then, it uses the interactive information produced in the retrieval process to make improvement on matching method based on concept map. It uses the interactive information produced in the retrieval process to calculate weight of users to search concept, which enhances the knowledge matching efficiency of concept map. Through test, it compares that intelligent knowledge retrieval system based on ontology has double advantages with high intelligence and efficiency.

APA, Harvard, Vancouver, ISO, and other styles

19

Liu, Wei Wei, and Shu Ying Shen. "Research on Design Knowledge Acquisition of Manipulator Based on Adaptive Genetic Algorithm." Advanced Materials Research 591-593 (November 2012): 2376–80. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.2376.

Full text

Abstract:

With the purpose of conducting new product development or designing variant product quickly, designers need to acquire relevant design knowledge and historical experience; thus, design knowledge acquisition arose at the historic moment. In the light of basic principles, characteristics and process of adaptive genetic algorithm, some kind of design knowledge in repository need to be disposed with a series of operations such as selection, crossover and mutation etc. And with the desired requirements of that kind of mechanical products, we could get the similar design or sometimes the 'perfect' solution in the knowledge base. In this essay, we take manipulator for example to acquire design knowledge; it not only validates the effectiveness of this method but also provides a new reference of design knowledge acquisition in new product development.

APA, Harvard, Vancouver, ISO, and other styles

20

Chen, Rong-Chang, Shu-Ping Suen, and Jyun-Yang Li. "A Novel Knowledge-Based System Based on Combined Sociometry and Genetic Algorithm for Tutoring." Advanced Science Letters 19, no. 8 (August 1, 2013): 2225–29. http://dx.doi.org/10.1166/asl.2013.4957.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

PHAM, D. T., and H. H. ONDER. "A knowledge-based system for optimizing workplace layouts using a genetic algorithm." Ergonomics 35, no. 12 (December 1992): 1479–87. http://dx.doi.org/10.1080/00140139208967417.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Sadrzadeh, Amir. "Knowledge-Based Genetic Algorithm for Dynamic Machine–Tool Selection and Operation Allocation." Arabian Journal for Science and Engineering 39, no. 5 (March 21, 2014): 4315–23. http://dx.doi.org/10.1007/s13369-014-0980-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Wang, Hanli, Sam Kwong, Yaochu Jin, Wei Wei, and K. F. Man. "Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction." Fuzzy Sets and Systems 149, no. 1 (January 2005): 149–86. http://dx.doi.org/10.1016/j.fss.2004.07.013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Zhao, Yong, and Ye Zheng Liu. "Modeling Knowledge Employee’s Turnover Based on P-SVM." Advanced Materials Research 121-122 (June 2010): 825–31. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.825.

Full text

Abstract:

Knowledge employee’s turnover forecast is a multi-criteria decision-making problem involving various factors. In order to forecast accurately turnover of knowledge employees, the potential support vector machines(P-SVM) is introduced to develop a turnover forecast model. In the model development, a chaos algorithm and a genetic algorithm (GA) are employed to optimize P-SVM parameters selection. The simulation results show that the model based on potential support vector machine with chaos not only has much stronger generalization ability but also has the ability of feature selection.

APA, Harvard, Vancouver, ISO, and other styles

25

Ykhlef, Mourad, and Hebah ElGibreen. "Mining Pharmacy Database Using Evolutionary Genetic Algorithm." International Journal of Electronics and Telecommunications 56, no. 4 (November 1, 2010): 427–32. http://dx.doi.org/10.2478/v10177-010-0058-4.

Full text

Abstract:

Mining Pharmacy Database Using Evolutionary Genetic AlgorithmMedication management is an important process in pharmacy field. Prescribing errors occur upstream in the process, and their effects can be perpetuated in subsequent steps. Prescription errors are an important issue for which conflicts with another prescribed medicine could cause severe harm for a patient. In addition, due to the shortage of pharmacists and to contain the cost of healthcare delivery, time is also an important issue. Former knowledge of prescriptions can reduce the errors, and discovery of such knowledge requires data mining techniques, such as Sequential Pattern. Moreover, Evolutionary Algorithms, such as Genetic Algorithm (GA), can find good rules in short time, thus it can be used to discover the Sequential Patterns in Pharmacy Database. In this paper GA is used to assess patient prescriptions based on former knowledge of series of prescriptions in order to extract sequenced patterns and predict unusual activities to reduce errors in timely manner.

APA, Harvard, Vancouver, ISO, and other styles

26

Yao, Yuchun, Yan Wang, Lining Xing, and Hao Xu. "An optimization method of technological processes to complex products using knowledge-based genetic algorithm." Journal of Knowledge Management 19, no. 1 (February 9, 2015): 82–94. http://dx.doi.org/10.1108/jkm-11-2014-0454.

Full text

Abstract:

Purpose – This paper applies the knowledge-based genetic algorithm to solve the optimization problem in complex products technological processes. Design/methodology/approach – The knowledge-based genetic algorithm (KGA) is defined as a hybrid genetic algorithm (GA) which combined the GA model with the knowledge model. The GA model searches the feasible space of optimization problem based on the “neighborhood search” mechanism. The knowledge model discovers some knowledge from the previous optimization process, and applies the obtained knowledge to guide the subsequent optimization process. Findings – The experimental results suggest that the proposed KGA is feasible and available. The effective integration of GA model and knowledge model has greatly improved the optimization performance of KGA. Originality/value – The technological innovation of complex products is one of effective approaches to establish the core competitiveness in future. For this reason, the KGA is proposed to the technological processes optimization of complex products.

APA, Harvard, Vancouver, ISO, and other styles

27

Liu, Lian, and Li Hong Qiao. "Operation Sequencing Using Genetic Algorithm." Applied Mechanics and Materials 163 (April 2012): 57–61. http://dx.doi.org/10.4028/www.scientific.net/amm.163.57.

Full text

Abstract:

Operation sequencing is one of the most important tasks in process planning. The sequencing procedures associate manufacturing features from 3D CAD models and machining methods together to satisfy certain manufacturing process constraints. In order to simplify process constraint aggregations, two types of constraint matrixes, feature constraint matrix and the operation constraint matrix, are proposed in this paper, which take into account of the compulsive constraints, such as geometric topology constraints, manufacturing process knowledge criteria, custom compulsive constraints and so forth. Accordingly, an iterative genetic algorithm is proposed, which is naturally used in the manufacturing feature level and operation level. In the manufacturing feature level, feasible feature sequences are generated based on the analysis of feature constraint matrix. In the operation level, the information that is contained in the machining operation such as machine tools, set-ups and cutting tools is considered to optimize the operation sequences based on the results acquired in the feature level. Compared with the traditional simple genetic algorithm, the iterative genetic algorithm is proved to be superior in shortening the operation sequencing time.

APA, Harvard, Vancouver, ISO, and other styles

28

Jian Gao, Rong Chen, and Yaqing Liu. "A Knowledge-based Genetic Algorithm for Permutation Flowshop Scheduling Problems with Multiple Factories." International Journal of Advancements in Computing Technology 4, no. 7 (April 30, 2012): 121–29. http://dx.doi.org/10.4156/ijact.vol4.issue7.13.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Guo, Yi Nan, Yong Lin, Mei Yang, and Shu Guo Zhang. "User’s Preference Aggregation Based on Parallel Interactive Genetic Algorithms." Applied Mechanics and Materials 34-35 (October 2010): 1159–64. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.1159.

Full text

Abstract:

In traditional interactive genetic algorithms, high-quality optimal solution is hard to be obtained due to small population size and limited evolutional generations. Aming at above problems, a parallel interactive genetic algorithm based on knowledge migration is proposed. During the evolution, the number of the populations is more than one. Evolution information can be exchanged between every two populations so as to guide themselves evolution. In order to realize the freedom communication, IP multicast is adopted as the transfer protocol to find out the similar users instead of traditional TCP/IP communication mode. Taken the fashion evolutionary design system as test platform, the results indicate that the IP multicast-based parallel interactive genetic algorithm has better population diversity. It also can alleviate user fatigue and speed up the convergence.

APA, Harvard, Vancouver, ISO, and other styles

30

Zhang, Ying, and Alice M. Agogino. "Hierarchical component-based representations for evolving microelectromechanical systems designs." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 25, no. 1 (October 7, 2010): 41–55. http://dx.doi.org/10.1017/s0890060410000168.

Full text

Abstract:

AbstractIn this paper we present a genotype representation method for improving the performance of genetic-algorithm-based optimal design and synthesis of microelectromechanical systems. The genetic algorithm uses a hierarchical component-based genotype representation, which incorporates specific engineering knowledge into the design optimization process. Each microelectromechanical system component is represented by a gene with its own parameters defining its geometry and the way it can be modified from one generation to the next. The object-oriented genotype structures efficiently describe the hierarchical nature typical of engineering designs. They also encode knowledge-based constraints that prevent the genetic algorithm from wasting time exploring inappropriate regions of the search space. The efficiency of the hierarchical component-based genotype representation is demonstrated with surface-micromachined resonator designs.

APA, Harvard, Vancouver, ISO, and other styles

31

Srinivasan, Sujatha, and Sivakumar Ramakrishnan. "A hybrid agent based virtual organization for studying knowledge evolution in social systems." Artificial Intelligence Research 1, no. 2 (September 24, 2012): 99. http://dx.doi.org/10.5430/air.v1n2p99.

Full text

Abstract:

Social modeling applies computational methods and techniques to the analysis of social processes and human behavior.Cultural algorithms (CA’s) are evolutionary systems which utilize agent technology and which supports any evolutionarystrategy like genetic algorithm, evolutionary algorithm or swarm intelligence or ant algorithms. CA’s have been used formodeling the evolution of complex social systems, for re-engineering rule based systems, for data mining, and for solvingoptimization problems. In the current study a cultural algorithm framework is used to model an Agent Based VirtualOrganization (ABVO) for studying the dynamics of a social system at micro as well as macro level. Research gap exists indefining a concrete and systematic method for evaluating and validating Agent Based Social Systems (ABSS). Also theknowledge evolution process at micro and macro levels of an organization needs further exploration. The proposed CA isapplied to the problem of multi-objective optimization (MOO) of classification rules. The evolutionary knowledgeproduced by the agents in creating the rules is accepted into the belief space of the CA and macro evolution takes place.The belief space in turn influences the agents in successive generations. The rules created by the individuals and theknowledge sources created during evolution provide a concrete method to evaluate both the individuals as well as thewhole social system. The feasibility of the system has been tested on bench mark data sets and the results are encouraging.

APA, Harvard, Vancouver, ISO, and other styles

32

Osman, Mohd Haniff, Zulkifli Mohd Nopiah, Shahrum Abdullah, and Izamarlina Asshaari. "Genetic Algorithm-Based Fatigue Data Editing Technique." Applied Mechanics and Materials 663 (October 2014): 431–36. http://dx.doi.org/10.4028/www.scientific.net/amm.663.431.

Full text

Abstract:

Durability testing is an essential process node in automotive component design analysis. The test associated with loading history can be accelerated if the fatigue data editing approach is considered for simplifying the given history. Even though they have been proven, unfortunately, the existing editing techniques involve complex mechanisms (e.g. abrupt detection, Fourier transformation and wavelet analysis), which are complicated in nature and which demand high computational costs. Therefore, this paper is a proposal of a simple technique that makes use of the rule-based fatigue segment classifier when deciding which parts of the history need to be removed. Rules representing the new labelling practice have been generated based on the classification data mining framework. In the context of this study, a rule set represents a group of undiscovered relationship between time domain statistical parameters and damage level. A dataset consisting of an equal length of fatigue segments trained using a multi-objective approach called the Elitist Non-dominated Sorting in Genetic Algorithm (NSGA-II) for seeking several optimal sets of rules (i.e., classifiers) by maximizing predictive accuracy and comprehensibility. The number of attributes underlying the rule set is referred to for final classifier selection where the fitter solution serves as the proposed editing technique. Comparison results on strain-stress cycle properties for the edited history and the full-length version shows that the proposed technique is suitable for fatigue data editing. Moreover, it has an additional benefit that no prior requirement on the frequency or time-frequency analysis is needed, providing the damage level of fatigue segments rapidly and the discovering of linguistic knowledge as a novelty.

APA, Harvard, Vancouver, ISO, and other styles

33

HUO, HONG-WEI, VOJISLAV STOJKOVIC, and QIAO-LUAN XIE. "A QUANTUM-INSPIRED GENETIC ALGORITHM BASED ON PROBABILISTIC CODING FOR MULTIPLE SEQUENCE ALIGNMENT." Journal of Bioinformatics and Computational Biology 08, no. 01 (February 2010): 59–75. http://dx.doi.org/10.1142/s0219720010004549.

Full text

Abstract:

Quantum parallelism arises from the ability of a quantum memory register to exist in a superposition of base states. Since the number of possible base states is 2n, where n is the number of qubits in the quantum memory register, one operation on a quantum computer performs what an exponential number of operations on a classical computer performs. The power of quantum algorithms comes from taking advantages of quantum parallelism. Quantum algorithms are exponentially faster than classical algorithms. Genetic optimization algorithms are stochastic search algorithms which are used to search large, nonlinear spaces where expert knowledge is lacking or difficult to encode. QGMALIGN — a probabilistic coding based quantum-inspired genetic algorithm for multiple sequence alignment is presented. A quantum rotation gate as a mutation operator is used to guide the quantum state evolution. Six genetic operators are designed on the coding basis to improve the solution during the evolutionary process. The experimental results show that QGMALIGN can compete with the popular methods, such as CLUSTALX and SAGA, and performs well on the presenting biological data. Moreover, the addition of genetic operators to the quantum-inspired algorithm lowers the cost of overall running time.

APA, Harvard, Vancouver, ISO, and other styles

34

Vijendra, Singh, and Sahoo Laxman. "Symmetry Based Automatic Evolution of Clusters: A New Approach to Data Clustering." Computational Intelligence and Neuroscience 2015 (2015): 1–21. http://dx.doi.org/10.1155/2015/796276.

Full text

Abstract:

We present a multiobjective genetic clustering approach, in which data points are assigned to clusters based on new line symmetry distance. The proposed algorithm is called multiobjective line symmetry based genetic clustering (MOLGC). Two objective functions, first the Davies-Bouldin (DB) index and second the line symmetry distance based objective functions, are used. The proposed algorithm evolves near-optimal clustering solutions using multiple clustering criteria, without a priori knowledge of the actual number of clusters. The multiple randomizedKdimensional (Kd) trees based nearest neighbor search is used to reduce the complexity of finding the closest symmetric points. Experimental results based on several artificial and real data sets show that proposed clustering algorithm can obtain optimal clustering solutions in terms of different cluster quality measures in comparison to existing SBKM and MOCK clustering algorithms.

APA, Harvard, Vancouver, ISO, and other styles

35

Pimenov, Viktor, and Ilia Pimenov. "Interpretation of a trained neural network based on genetic algorithms." Information and Control Systems, no. 6 (December 15, 2020): 12–20. http://dx.doi.org/10.31799/1684-8853-2020-6-12-20.

Full text

Abstract:

Introduction: Artificial intelligence development strategy involves the use of deep machine learning algorithms in order to solve various problems. Neural network models trained on specific data sets are difficult to interpret, which is due to the “black box” approach when knowledge is formed as a set of interneuronal connection weights. Purpose: Development of a discrete knowledge model which explicitly represents information processing patterns encoded by connections between neurons. Methods: Adaptive quantization of a feature space using a genetic algorithm, and construction of a discrete model for a multidimensional OLAP cube with binary measures. Results: A genetic algorithm extracts a discrete knowledge carrier from a trained neural network. An individual's chromosome encodes a combination of values of all quantization levels for the measurable object properties. The head gene group defines the feature space structure, while the other genes are responsible for setting up the quantization of a multidimensional space, where each gene is responsible for one quantization threshold for a given variable. A discrete model of a multidimensional OLAP cube with binary measures explicitly represents the relationships between combinations of object feature values and classes. Practical relevance: For neural network prediction models based on a training sample, genetic algorithms make it possible to find the effective value of the feature space volume for the combinations of input feature values not represented in the training sample whose volume is usually limited. The proposed discrete model builds unique images of each class based on rectangular maps which use a mesh structure of gradations. The maps reflect the most significant integral indicators of classes that determine the location and size of a class in a multidimensional space. Based on a convolution of the constructed class images, a complete system of production decision rules is recorded for the preset feature gradations.

APA, Harvard, Vancouver, ISO, and other styles

36

Passone, S., P. W. H. Chung, and V. Nassehi. "Incorporating domain-specific knowledge into a genetic algorithm to implement case-based reasoning adaptation." Knowledge-Based Systems 19, no. 3 (July 2006): 192–201. http://dx.doi.org/10.1016/j.knosys.2005.07.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Fan, Shun Cheng, and Jin Feng Wang. "Multi-Objective Decision and Optimization of Process Routing Based on Genetic Algorithm (GA)." Advanced Materials Research 457-458 (January 2012): 1494–98. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.1494.

Full text

Abstract:

Based on the previous research on decision and optimization of process routing,this paper analyzes the characteristic of process knowledge, establishes the representation methods of process knowledge based on feature. According to the constraints mong process knowledge, decision space of process routing based on process constraints is constructed which improved search efficiency of GA. In allusion to the uncertainty of decision of process routing, the multi-objective optimization function is established, and GA is applied to decision and optimization of process routing. Process routing is optimized using the reasonable coding strategy,objective function, crossover and mutation algorithm. An case is offered to illustrate the process of decision and optimization of process routing based on GA.

APA, Harvard, Vancouver, ISO, and other styles

38

Su, Fei, and Zhe Fan. "Flipped Classroom Design of College Ideological and Political Courses Based on Long Short-Term Memory Networks." Scientific Programming 2021 (July 12, 2021): 1–8. http://dx.doi.org/10.1155/2021/6971906.

Full text

Abstract:

The advancement and rising of information technology have promoted the flipped classroom in an effective way. It flips knowledge transfer and knowledge internalization from two levels of teaching structure and teaching process, reversing the traditional teaching knowledge transfer in class and knowledge deepening after class from time and space. Although the use of flipped classrooms in ideological and political theory courses is relatively uncommon in colleges and universities, realistic teaching and related study findings in some colleges and universities provide some reference value for the use of flipped classrooms in ideological and political theory courses. As a result, the short- and long-time memory network-based flipped classroom design algorithm for ideological and political courses in colleges and universities has a wide range of applications. A neural network prediction model based on a hybrid genetic algorithm is developed in this paper. The hybrid genetic algorithm is used in this model to determine the optimal dropout probability and the number of cells in the hidden layer of the neural network. The hybrid genetic algorithm will lengthen the memory neural network to predict the teaching quality of root mean square error between real value and predictive value as a fitness function, in the process of optimization, genetic algorithm convergence to the local optimal solution of the area.

APA, Harvard, Vancouver, ISO, and other styles

39

Xu, Yang, Mingming Zeng, Quanhui Liu, and Xiaofeng Wang. "A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/867149.

Full text

Abstract:

Multilevel association rules mining is an important domain to discover interesting relations between data elements with multiple levels abstractions. Most of the existing algorithms toward this issue are based on exhausting search methods such as Apriori, and FP-growth. However, when they are applied in the big data applications, those methods will suffer for extreme computational cost in searching association rules. To expedite multilevel association rules searching and avoid the excessive computation, in this paper, we proposed a novel genetic-based method with three key innovations. First, we use the category tree to describe the multilevel application data sets as the domain knowledge. Then, we put forward a special tree encoding schema based on the category tree to build the heuristic multilevel association mining algorithm. As the last part of our design, we proposed the genetic algorithm based on the tree encoding schema that will greatly reduce the association rule search space. The method is especially useful in mining multilevel association rules in big data related applications. We test the proposed method with some big datasets, and the experimental results demonstrate the effectiveness and efficiency of the proposed method in processing big data. Moreover, our results also manifest that the algorithm is fast convergent with a limited termination threshold.

APA, Harvard, Vancouver, ISO, and other styles

40

Shrivastava, Prateek, and Khemraj Deshmukh. "SIMULATION OF PSO BASED APPROACH FOR CMOL CELL ASSIGNMENT PROBLEM." International Journal of Research -GRANTHAALAYAH 3, no. 5 (May 31, 2015): 1–12. http://dx.doi.org/10.29121/granthaalayah.v3.i5.2015.3009.

Full text

Abstract:

Particle swarm optimization (PSO) approach is used over genetic algorithms (GAS) to solve many of the same kinds of problems. This optimization technique does not suffer, however, from some of GA’s difficulties; interaction in the group enhances rather than detracts from progress toward the solution. Further, a particle swarm system has memory, which the genetic algorithm does not have. In particle swarm optimization, individuals who fly past optima are tugged to return toward them; knowledge of good solutions is retained by all particles. The genetic algorithm works with the concept of chromosomes having gene where each gene act as a block of one solution. This is totally based on the solution which is followed by crossover and then mutation and finally reaches to fitness. The best fitness will be considered as a result and implemented in the practical area. Due to some drawbacks and problems exist in the genetic algorithm implemented, scientists moved to the other algorithm technique which is apparently based on the flock of birds moving to the target. This effectively overcome the shortcomings of GA and provides better fitness solutions to implement in the circuit.

APA, Harvard, Vancouver, ISO, and other styles

41

Pan, Hui, and Tao Wang. "Optimization of Object-Based Knowledge Mesh Structure Based on Time Performance." Applied Mechanics and Materials 380-384 (August 2013): 2761–64. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2761.

Full text

Abstract:

To optimize the structure of enterprise information systems, this paper deals with a new approach to optimize the structure of object-based knowledge mesh (OKM) based on time performance, which is the formal representation of enterprise information systems. Firstly, the relationships between knowledge points are discussed, and time performance of knowledge point relationships is discussed. Then, the representation of knowledge point construction based on the binary tree is proposed, which solves the coding problem of optimization. And then, based on the improved immune genetic algorithm, the structure of OKM is optimized. Finally, the new approach is exemplified and verified, and the optimized the structure of OKM is obtained, which lays the foundation for performance optimization of enterprise information systems.

APA, Harvard, Vancouver, ISO, and other styles

42

Yin, XueHong. "Construction of Student Information Management System Based on Data Mining and Clustering Algorithm." Complexity 2021 (May 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/4447045.

Full text

Abstract:

Data mining is a new technology developed in recent years. Through data mining, people can discover the valuable and potential knowledge hidden behind the data and provide strong support for scientifically making various business decisions. This paper applies data mining technology to the college student information management system, mines student evaluation information data, uses data mining technology to design student evaluation information modules, and digs out the factors that affect student development and the various relationships between these factors. Predictive assessment of knowledge and personalized teaching decision-making provide the basis. First, the general situation of genetic algorithm and fuzzy genetic algorithm is introduced, and then, an improved genetic fuzzy clustering algorithm is proposed. Compared with traditional clustering algorithm and improved genetic fuzzy clustering algorithm, the effectiveness of the algorithm proposed in this paper is proved. Based on the analysis system development related tools and methods, in response to the needs of the student information management system, a simple student information management system is designed and implemented, which provides a platform and data source for the next application of clustering algorithm for performance analysis. Finally, clustering the students’ scores with a clustering algorithm based on fuzzy genetic algorithm, the experimental results show that this method can better analyze the students’ scores and help relevant teachers and departments make decisions.

APA, Harvard, Vancouver, ISO, and other styles

43

HOMCHAUDHURI, BAISRAVAN, MANISH KUMAR, and KELLY COHEN. "GENETIC ALGORITHM BASED SIMULATION–OPTIMIZATION FOR FIGHTING WILDFIRES." International Journal of Computational Methods 10, no. 06 (May 2, 2013): 1350035. http://dx.doi.org/10.1142/s0219876213500357.

Full text

Abstract:

Wildfire is one of the most significant disturbances responsible for reshaping the terrain and changing the ecosystem of a particular region. Its detrimental effects on environment as well as human lives and properties, and growing trend in terms of frequency and intensity of wildfires over the last decade have necessitated the development of efficient forest fire management techniques. During the last three decades, Forest Fire Decision Support Systems (FFDSS) have been developed to help in the decision-making processes during forest fires by providing necessary information on fire detection, their status and behavior, and other aspects of forest fires. However, most of these decision support systems lack the capability of developing intelligent fire suppression strategies based upon current status and predicted behavior of forest fire. This paper presents an approach for development of efficient fireline building strategies via intelligent resource allocation. A Genetic Algorithm based approach has been proposed in this paper for resource allocation and optimum fireline building that minimizes the total damage due to wildland fires. The approach is based on a simulation–optimization technique in which the Genetic Algorithm uses advanced forest fire propagation models based upon Huygens principles for evaluation of cost index of its solutions. Both homogeneous and heterogeneous environmental conditions have been considered. Uncertainties in weather conditions as well as imperfect knowledge about exact vegetation and topographical conditions make exact prediction of wildfires very difficult. The paper incorporates Monte-Carlo simulations to develop robust strategies in uncertain conditions. Extensive simulations demonstrate the effectiveness of the proposed approach in efficient resource allocation for fighting complex wildfires in uncertain and dynamic conditions.

APA, Harvard, Vancouver, ISO, and other styles

44

Huang, Xiao Rong, Shun Sheng Guo, and Li Bo Sun. "Project Team Formation Based on Knowledge and Cooperation Degree." Applied Mechanics and Materials 44-47 (December 2010): 3143–47. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3143.

Full text

Abstract:

To aim at the project team formation problem, this study proposes a formation model based on knowledge and cooperation degree. The ability of individual member and cooperation degree of team members are considered. In addition ,it presents a way of measuring candidate’s ability about knowledge, and establishes a collaborative model to measure the cooperation degree between team members. Furthermore, a calculation method of knowledge and cooperation degree is proposed, and then a mathematical model is established. Finally it presented a solution base on Genetic Algorithm for this model.

APA, Harvard, Vancouver, ISO, and other styles

45

Wang, Ping, and Tai Shan Yan. "A Knowledge Rule Mining Method for the Evaluation of Library Service Quality Based on Genetic Algorithm." Advanced Materials Research 532-533 (June 2012): 1588–92. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1588.

Full text

Abstract:

In this study, the evaluation index system of library service quality is established and the representation method of knowledge rule is analyzed firstly. Then, a knowledge rule mining method for the evaluation of library service quality based on an improved genetic algorithm is proposed. In the algorithm, selection operator, help operator, crossover operator and mutation operator are used to generate new knowledge rules. Knowledge rules are evaluated by their accuracy, coverage and reliability. Experimental results show that this knowledge rule mining method is feasible and valid. It is helpful for us to evaluate the library service quality fairly and objectively.

APA, Harvard, Vancouver, ISO, and other styles

46

Xue, Xingsi, Xiaojing Wu, and Junfeng Chen. "Optimizing Ontology Alignment Through an Interactive Compact Genetic Algorithm." ACM Transactions on Management Information Systems 12, no. 2 (June 2021): 1–17. http://dx.doi.org/10.1145/3439772.

Full text

Abstract:

Ontology provides a shared vocabulary of a domain by formally representing the meaning of its concepts, the properties they possess, and the relations among them, which is the state-of-the-art knowledge modeling technique. However, the ontologies in the same domain could differ in conceptual modeling and granularity level, which yields the ontology heterogeneity problem. To enable data and knowledge transfer, share, and reuse between two intelligent systems, it is important to bridge the semantic gap between the ontologies through the ontology matching technique. To optimize the ontology alignment’s quality, this article proposes an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique, which consists of an automatic ontology matching process based on a Compact Genetic Algorithm (CGA) and a collaborative user validating process based on an argumentation framework. First, CGA is used to automatically match the ontologies, and when it gets stuck in the local optima, the collaborative validation based on the multi-relationship argumentation framework is activated to help CGA jump out of the local optima. In addition, we construct a discrete optimization model to define the ontology matching problem and propose a hybrid similarity measure to calculate two concepts’ similarity value. In the experiment, we test the performance of ICGA with the Ontology Alignment Evaluation Initiative’s interactive track, and the experimental results show that ICGA can effectively determine the ontology alignments with high quality.

APA, Harvard, Vancouver, ISO, and other styles

47

Li, Yi, and Zhengxing Sun. "Articulated Human Motion Tracking Using Sequential Immune Genetic Algorithm." Mathematical Problems in Engineering 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/921510.

Full text

Abstract:

We formulate human motion tracking as a high-dimensional constrained optimization problem. A novel generative method is proposed for human motion tracking in the framework of evolutionary computation. The main contribution is that we introduce immune genetic algorithm (IGA) for pose optimization in latent space of human motion. Firstly, we perform human motion analysis in the learnt latent space of human motion. As the latent space is low dimensional and contents the prior knowledge of human motion, it makes pose analysis more efficient and accurate. Then, in the search strategy, we apply IGA for pose optimization. Compared with genetic algorithm and other evolutionary methods, its main advantage is the ability to use the prior knowledge of human motion. We design an IGA-based method to estimate human pose from static images for initialization of motion tracking. And we propose a sequential IGA (S-IGA) algorithm for motion tracking by incorporating the temporal continuity information into the traditional IGA. Experimental results on different videos of different motion types show that our IGA-based pose estimation method can be used for initialization of motion tracking. The S-IGA-based motion tracking method can achieve accurate and stable tracking of 3D human motion.

APA, Harvard, Vancouver, ISO, and other styles

48

Piroozfard, Hamed, Kuan Yew Wong, and Adnan Hassan. "A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems." Journal of Optimization 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/7319036.

Full text

Abstract:

Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications. The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex andNP-hard problems. This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan. In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules. In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation. A machine based precedence preserving order-based crossover was proposed to generate the offspring. Furthermore, a simulated annealing based neighborhood search technique was used to improve the local exploitation ability of the algorithm and to increase its population diversity. In order to prove the efficiency and effectiveness of the proposed algorithm, numerous benchmarked instances were collected from the Operations Research Library. Computational results of the proposed hybrid genetic algorithm demonstrate its effectiveness.

APA, Harvard, Vancouver, ISO, and other styles

49

Deng, Yaohua, Qiwen Lu, Jiayuan Chen, Sicheng Chen, Liming Wu, and Luxin Tang. "Study on the Extraction Method of Deformation Influence Factors of Flexible Material Processing Based on Information Entropy." Advances in Mechanical Engineering 6 (January 1, 2014): 547947. http://dx.doi.org/10.1155/2014/547947.

Full text

Abstract:

Through analyzing the flexible material processing (FMP) deformation factors, it is pointed out that without a choice of deformation influence quantity would increase the compensation control predict model system input. In order to reduce the count of spatial dimensions of knowledge, we proposed the method by taking the use of FMP deformation compensation control knowledge extraction, which is based on decision table (DT) attribute reduction, deriving the algorithm that is based on information entropy attribute importance, to find the dependencies between attributes through attribute significance (AS) and to extract the intrinsic attributes which is the most close to deformation compensation control decision making. Finally, through an example presented in this paper to verify the efficiency of RS control knowledge extraction method. Compared with the Pawlak method and genetic extraction algorithm, the prediction accuracy of after reduction data is 0.55% less than Pawlak method and 3.64% higher than the genetic extraction algorithm; however, the time consumption of forecast calculation is 30.3% and 11.53% less than Pawlak method and genetic extraction algorithm, respectively. Knowledge extraction entropy methods presented in this paper have the advantages of fast calculating speed and high accuracy and are suitable for FMP deformation compensation of online control.

APA, Harvard, Vancouver, ISO, and other styles

50

De Gaspari, Alessandro, and Sergio Ricci. "Knowledge-Based Shape Optimization of Morphing Wing for More Efficient Aircraft." International Journal of Aerospace Engineering 2015 (2015): 1–19. http://dx.doi.org/10.1155/2015/325724.

Full text

Abstract:

An optimization procedure for the shape design of morphing aircraft is presented. The process is coupled with a knowledge-based framework combining parametric geometry representation, multidisciplinary modelling, and genetic algorithm. The parameterization method exploits the implicit properties of the Bernstein polynomial least squares fitting to allow both local and global shape control. The framework is able to introduce morphing shape changes in a feasible way, taking into account the presence of structural parts, such as the wing-box, the physical behaviour of the morphing skins, and the effects that these modifications have on the aerodynamic performances. It inherits CAD capabilities of generating 3D deformed morphing shapes and it is able to automatically produce aerodynamic and structural models linked to the same parametric geometry. Dedicated crossover and mutation strategies are used to allow the parametric framework to be efficiently incorporated into the genetic algorithm. This procedure is applied to the shape design of Reference Aircraft (RA) and to the assessment of the potential benefits that morphing devices can bring in terms of aircraft performances. It is adopted for the design of a variable camber morphing wing to investigate the effect of conformal leading and trailing edge control surfaces. Results concerning four different morphing configurations are reported.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Knowledge-based genetic algorithm'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles