Relevant bibliographies by topics / Knowledge-based genetic algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Knowledge-based genetic algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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

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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.

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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.

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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.
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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.

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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.

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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.

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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.

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Dissertations / Theses on the topic "Knowledge-based genetic algorithm"

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Makai, Matthew Charles. "Incorporating Design Knowledge into Genetic Algorithm-based White-Box Software Test Case Generators." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32029.

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This thesis shows how to incorporate Unified Modeling Language sequence diagrams into genetic algorithm-based automated test case generators to increase the code coverage of their resulting test cases. Automated generation of test data through evolutionary testing was proven feasible in prior research studies. In those previous investigations, the metrics used for determining the test generation method effectiveness were the percentages of testing statement and branch code coverage achieved. However, the code coverage realized in those preceding studies often converged at suboptimal percentages due to a lack of guidance in conditional statements. This study compares the coverage percentages of 16 different Java programs when test cases are automatically generated with and without incorporating associated UML sequence diagrams. It introduces a tool known as the Evolutionary Test Case Generator, or ETCG, an automatic test case generator based on genetic algorithms that provides the ability to incorporate sequence diagrams to direct the heuristic search process and facilitate evolutionary testing. When the generator uses sequence diagrams, the resulting test cases showed an average improvement of 21% in branch coverage and 8% in statement coverage over test cases produced without using sequence diagrams.
Master of Science
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Mirhosseyni, Seyed Hamid Layegh. "A hybrid fuzzy knowledge-based expert system and genetic algorithm for efficient selection and assignment of material handling equipment." Thesis, University of Nottingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495071.

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The selection of the most appropriate Material Handling Equipment (MHE) types for handling operations within a production plant and the efficient configuration of them in order to attain an optimal solution for the entire problem is still a challenging and unsolved problem. As MH has a significant impact on the efficiency of the total production system, efficiently resolving it is a key task.
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Dam, Hai Huong Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "A scalable evolutionary learning classifier system for knowledge discovery in stream data mining." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38865.

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Data mining (DM) is the process of finding patterns and relationships in databases. The breakthrough in computer technologies triggered a massive growth in data collected and maintained by organisations. In many applications, these data arrive continuously in large volumes as a sequence of instances known as a data stream. Mining these data is known as stream data mining. Due to the large amount of data arriving in a data stream, each record is normally expected to be processed only once. Moreover, this process can be carried out on different sites in the organisation simultaneously making the problem distributed in nature. Distributed stream data mining poses many challenges to the data mining community including scalability and coping with changes in the underlying concept over time. In this thesis, the author hypothesizes that learning classifier systems (LCSs) - a class of classification algorithms - have the potential to work efficiently in distributed stream data mining. LCSs are an incremental learner, and being evolutionary based they are inherently adaptive. However, they suffer from two main drawbacks that hinder their use as fast data mining algorithms. First, they require a large population size, which slows down the processing of arriving instances. Second, they require a large number of parameter settings, some of them are very sensitive to the nature of the learning problem. As a result, it becomes difficult to choose a right setup for totally unknown problems. The aim of this thesis is to attack these two problems in LCS, with a specific focus on UCS - a supervised evolutionary learning classifier system. UCS is chosen as it has been tested extensively on classification tasks and it is the supervised version of XCS, a state of the art LCS. In this thesis, the architectural design for a distributed stream data mining system will be first introduced. The problems that UCS should face in a distributed data stream task are confirmed through a large number of experiments with UCS and the proposed architectural design. To overcome the problem of large population sizes, the idea of using a Neural Network to represent the action in UCS is proposed. This new system - called NLCS { was validated experimentally using a small fixed population size and has shown a large reduction in the population size needed to learn the underlying concept in the data. An adaptive version of NLCS called ANCS is then introduced. The adaptive version dynamically controls the population size of NLCS. A comprehensive analysis of the behaviour of ANCS revealed interesting patterns in the behaviour of the parameters, which motivated an ensemble version of the algorithm with 9 nodes, each using a different parameter setting. In total they cover all patterns of behaviour noticed in the system. A voting gate is used for the ensemble. The resultant ensemble does not require any parameter setting, and showed better performance on all datasets tested. The thesis concludes with testing the ANCS system in the architectural design for distributed environments proposed earlier. The contributions of the thesis are: (1) reducing the UCS population size by an order of magnitude using a neural representation; (2) introducing a mechanism for adapting the population size; (3) proposing an ensemble method that does not require parameter setting; and primarily (4) showing that the proposed LCS can work efficiently for distributed stream data mining tasks.
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Garcés, Monge Luis. "Knowledge-based configuration : a contribution to generic modeling, evaluation and evolutionary optimization." Thesis, Ecole nationale des Mines d'Albi-Carmaux, 2019. http://www.theses.fr/2019EMAC0003/document.

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Dans un contexte de personnalisation de masse, la configuration concourante du produit et de son processus d’obtention constituent un défi industriel important : de nombreuses options ou alternatives, de nombreux liens ou contraintes et un besoin d’optimisation des choix réalisés doivent être pris en compte. Ce problème est intitulé O-CPPC (Optimization of Concurrent Product and Process Configuration). Nous considérons ce problème comme un CSP (Constraints Satisfaction Problem) et l’optimisons avec des algorithmes évolutionnaires. Un état de l’art fait apparaître : i) que la plupart des travaux de recherche sont illustrés sur des exemples spécifiques à un cas industriel ou académique et peu représentatifs de la diversité existante ; ii) un besoin d’amélioration des performances d’optimisation afin de gagner en interactivité et faire face à des problèmes de taille plus conséquente. En réponse au premier point, ces travaux de thèse proposent les briques d’un modèle générique du problème O-CPPC. Ces briques permettent d’architecturer le produit et son processus d’obtention. Ce modèle générique est utilisé pour générer un benchmark réaliste pour évaluer les algorithmes d’optimisation. Ce benchmark est ensuite utilisé pour analyser la performance de l’approche évolutionnaire CFB-EA. L’une des forces de cette approche est de proposer rapidement un front de Pareto proche de l’optimum. Pour répondre au second point, une amélioration de cette méthode est proposée puis évaluée. L’idée est, à partir d’un premier front de Pareto approximatif déterminé très rapidement, de demander à l’utilisateur de choisir une zone d’intérêt et de restreindre la recherche de solutions uniquement sur cette zone. Cette amélioration entraine des gains de temps de calcul importants
In a context of mass customization, the concurrent configuration of the product and its production process constitute an important industrial challenge: Numerous options or alternatives, numerous links or constraints and a need to optimize the choices made. This problem is called O-CPPC (Optimization of Concurrent Product and Process Configuration). We consider this problem as a CSP (Constraints Satisfaction Problem) and optimize it with evolutionary algorithms. A state of the art shows that: i) most studies are illustrated with examples specific to an industrial or academic case and not representative of the existing diversity; ii) a need to improve optimization performance in order to gain interactivity and face larger problems. In response to the first point, this thesis proposes a generic model of the O-CPPC problem. This generic model is used to generate a realistic benchmark for evaluating optimization algorithms. This benchmark is then used to analyze the performance of the CFB-EA evolutionary approach. One of the strengths of this approach is to quickly propose a Pareto front near the optimum. To answer the second point, an improvement of this method is proposed and evaluated. The idea is, from a first approximate Pareto front, to ask the user to choose an area of interest and to restrict the search for solutions only on this area. This improvement results in significant computing time savings
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Aldridge, C. J., S. McKee, J. R. McDonald, S. J. Galloway, Keshav P. Dahal, M. E. Bradley, and J. F. Macqueen. "A knowledge-based genetic algorithm for unit commitment." 2001. http://hdl.handle.net/10454/3689.

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A genetic algorithm (GA) augmented with knowledge-based methods has been developed for solving the unit commitment economic dispatch problem. The GA evolves a population of binary strings which represent commitment schedules. The initial population of schedules is chosen using a method based on elicited scheduling knowledge. A fast rule-based dispatch method is then used to evaluate candidate solutions. The knowledge-based genetic algorithm is applied to a test system of ten thermal units over 24-hour time intervals, including minimum on/off times and ramp rates, and achieves lower cost solutions than Lagrangian relaxation in comparable computational time.
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Chien, Wan-jung, and 簡琬蓉. "A knowledge-based genetic algorithm for the job shop scheduling problem." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/83677200930084049591.

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碩士
南華大學
資訊管理學研究所
94
This study presents a novel use of attribution for the extraction of knowledge from job shop scheduling problem. Our algorithm improves the traditional GA and using knowledge to keep the quality of solution. Based on the knowledge, the search space will be leaded to a better search space. In addition, this study uses mutation to do local search and refresh the knowledge and population when the solution fall into local minimum. Based on those methods, our algorithm will have the intensification and diversification. Those can make the algorithm have good convergence and leap for the search space to find the better solution. The experiment results show that algorithm steadily and can find the approximate optimal solution. And the knowledge is useful in provide the gene selection information.
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Dahal, Keshav P., C. J. Aldridge, and S. J. Galloway. "Evolutionary hybrid approaches for generation scheduling in power systems." 2007. http://hdl.handle.net/10454/2323.

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Books on the topic "Knowledge-based genetic algorithm"

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International Conference on Knowledge-Based Intelligent Electronic Systems (2nd 1998 Adelaide, South Australia). 1998 second International Conference on Knowledge-Based Intelligent Electronic Systems: Proceedings : KES '98 : Adelaide, South Australia, 21-23 April, 1998. Edited by Jain L. C, Jain R. K, and Institute of Electrical and Electronics Engineers. Piscataway, New Jersey: IEEE, 1998.

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International Conference on Knowledge-Based Intelligent Information Engineering Systems (3rd 1999 Adelaide, South Australia). 1999 third International Conference on Knowledge-Based Intelligent Information Engineering Systems: Proceedings : KES '99 : Adelaide, South Australia, 31 August-1 September 1999. Edited by Jain L. C, Institute of Electrical and Electronics Engineers., and International Conference on Knowledge-Based Intelligent Electronic Systems. Piscataway, New Jersey: IEEE, 1999.

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Acquisition of Software Engineering Knowledge: Sweep, an Automatic Programming System Based on Genetic Programming and Cultural Algorithms (Series on Software Engineering and Knowledge Engineering). World Scientific Publishing Company, 2004.

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Book chapters on the topic "Knowledge-based genetic algorithm"

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Chang, Carl K., and Yujia Ge. "Genetic Algorithm Techniques and Applications in Management Systems." In Intelligent Knowledge-Based Systems, 1718–38. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/978-1-4020-7829-3_47.

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Fu, Zetian, Le Chen, Yonghong Guo, and Yongmei Guo. "BASED ON GENETIC ALGORITHM KNOWLEDGE ACQUISITION MODEL." In IFIP Advances in Information and Communication Technology, 1423–32. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0211-5_71.

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Tenorio, E., J. Gómez-Ruiz, J. I. Peláez, and J. M. Doña. "A Genetic Algorithm to Design Industrial Materials." In Knowledge-Based and Intelligent Information and Engineering Systems, 445–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15393-8_50.

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Ghosh, A., J. W. Tweedale, and A. Nafalski. "Optimizing Stressors Using Genetic Algorithm to Minimize Work-Related Stress." In Knowledge-Based Information Systems in Practice, 295–311. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13545-8_17.

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Farago, Paul, Sorin Hintea, Gabriel Oltean, and Lelia Festila. "A Double-Layer Genetic Algorithm for Gm-C Filter Design." In Knowledge-Based and Intelligent Information and Engineering Systems, 623–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15384-6_66.

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Oltean, Gabriel, Sorin Hintea, and Emilia Sipos. "A Genetic Algorithm-Based Multiobjective Optimization for Analog Circuit Design." In Knowledge-Based and Intelligent Information and Engineering Systems, 506–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04592-9_63.

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Matsumoto, Hideyuki, Kai Tun Lim, Chiaki Kuroda, Takehiro Yamaki, and Keigo Matsuda. "Application Methods for a Niche Genetic Algorithm for Design of Reactive Distillation Processes." In Knowledge-Based Information Systems in Practice, 313–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13545-8_18.

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Kim, Hee-Su, and Sung-Bae Cho. "Fashion Design Using Interactive Genetic Algorithm with Knowledge-based Encoding." In Knowledge Incorporation in Evolutionary Computation, 411–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-44511-1_19.

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Tsai, Ming-Feng, Chun-Yi Lu, Churn-Jung Liau, and Tuan-Fang Fan. "Social Network Clustering by Using Genetic Algorithm: A Case Study." In Trends in Applied Knowledge-Based Systems and Data Science, 294–304. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42007-3_25.

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Kim, Hee-Su, and Sung-Bae Cho. "Genetic Algorithm with Knowledge-Based Encoding for Interactive Fashion Design." In PRICAI 2000 Topics in Artificial Intelligence, 404–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44533-1_42.

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Conference papers on the topic "Knowledge-based genetic algorithm"

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Mortezanezhad, Afsaneh, and Ebrahim Daneshifar. "Big-Data Clustering with Genetic Algorithm." In 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI). IEEE, 2019. http://dx.doi.org/10.1109/kbei.2019.8735076.

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Arani, A. A. Khodadoost, Ali Ghasemi, H. Karami, Mahdi Akhbari, and G. B. Gharehpetian. "Optimal Switching Algorithm for Different Topologies of 15-Level Inverter Using Genetic Algorithm." In 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI). IEEE, 2019. http://dx.doi.org/10.1109/kbei.2019.8734966.

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Wei, Pan, Zhu Wenliang, and Liu Sili. "Rough set knowledge reduction algorithm based on chaos genetic algorithm." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162134.

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Faridmoayer, Sogol, Mohammadreza Sharbaf, and Shekoufeh Kolahdouz-Rahimi. "Optimization of model transformation output using genetic algorithm." In 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI). IEEE, 2017. http://dx.doi.org/10.1109/kbei.2017.8324973.

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Etemadi, Mohammad, and Reza Haghighian. "Design Optimization of Wound Rotor Induction Motor Using Genetic Algorithm." In 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI). IEEE, 2019. http://dx.doi.org/10.1109/kbei.2019.8735061.

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Gao, Kai, Yuejun Tan, and Wei Pan. "Rough set knowledge reduction algorithm based on improved chaos genetic algorithm." In 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016. http://dx.doi.org/10.1109/ccdc.2016.7531043.

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Hou Ruidong, Zhang Xiaohui, Pan Wei, and Mao Ning. "Knowledge reduction algorithm for rough sets based on adaptive genetic algorithm." In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4598314.

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Akbari, Mehdi, and Habib Izadkhah. "Hybrid of genetic algorithm and krill herd for software clustering problem." In 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI). IEEE, 2019. http://dx.doi.org/10.1109/kbei.2019.8734939.

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Lo, C. H., Eric H. K. Fung, and Y. K. Wong. "Knowledge-Based Automatic Fault Detection in Flight Control System." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41495.

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There are various possible failures, like, actuator, sensor, or structural, which can occur on a sophisticated modern aircraft. In certain situations the need for an automatic fault detection system provides additional information about the status of the aircraft to assist pilots to compensate for failures. In this paper, we develop an intelligent technique based on fuzzy-genetic algorithm for automatically detecting failures in flight control system. The fuzzy-genetic algorithm is proposed to construct the automatic fault detection system for monitoring aircraft behaviors. Fuzzy system is employed to estimates the times and types of actuator failure. Genetic algorithms are used to generate an optimal fuzzy rule set based on the training data. The optimization capability of genetic algorithms provides and efficient and effective way to generate optimal fuzzy rules. Different types of actuator failure can be detected by the fuzzy-genetic algorithm based automatic fault detection system after tuning its rule table. Simulations with different actuator failures of the non-linear F-16 aircraft model are conducted to appraise the performance of the proposed automatic fault detection system.
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Mohammadi, Javad, Kamal Mirzaie, and Vali Derhami. "Parallel genetic algorithm based on GPU for solving quadratic assignment problem." In 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI). IEEE, 2015. http://dx.doi.org/10.1109/kbei.2015.7436107.

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