Adaptation of Evolutionary Algorithms for Decision Making on Building Construction Engineering (TSP Problem)

• Autorzy: Wazirali R. A. , Alzughaibi A. D. , Chaczko Z.

Identyfikatory

DOI

10.2478/eletel-2014-0015

• Języki publikacji: EN

Abstrakty

EN

The report revolve on building construction engineering and management, in which there are a lot of requirements such as well supervision and accuracy and being in position to forecast uncertainties that may arise and mechanisms to solve them. It also focuses on the way the building and construction can minimise the cost of building and wastages of materials. The project will be based of heuristic methods of Artificial Intelligence (AI). There are various evolution methods, but report focus on two experiments Pattern Recognition and Travelling Salesman Problem (TSP). The Pattern Recognition focuses Evolutionary Support Vector Machine Inference System for Construction Management. The construction is very dynamic are has a lot of uncertainties, no exact data this implies that the inference should change according to the environment so that it can fit the reality, therefore there a need of Support Vector Machine Inference System to solve these problems. TSP focus on reducing cost of building construction engineering and also reduces material wastages, through its principals of finding the minimum cost path of the salesman.

Słowa kluczowe

EN

TSP; genetic algorithms; GA; support vector machine; SVM

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2014
• Tom: Vol. 60, No. 1
• Strony: 125--128
• Opis fizyczny: Bibliogr. 8 poz.

Twórcy

autor

Wazirali R. A.

• University of Technology (UTS) Sydney, NSW, Australia

autor

Alzughaibi A. D.

• Taiba University, Saudi Arabia, Almadinah

autor

Chaczko Z.

• University of Technology (UTS) Sydney, NSW, Australia

Bibliografia

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• [2] I. Mareels and J. W. Polderman, Adaptive Systems: An Introduction. Boston, Massachusetts: Birkhauser, 1996, pp. 1–3.
• [3] L. Chun-fu, “Fuzzy support vector machines,” Ph.D. dissertation, Departament of Electrical Engineering, National Taiwan University, Taipei, Taiwan, 2004.
• [4] B. Yang and N. Yau, “Integrating case-based reasoning and expert system techniques for solving experience-oriented problems,” Journal of the Chinese Institute of Engineers, vol. 23, no. 1, pp. 83–95, 2000.
• [5] C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, pp. 121–167, 1998.
• [6] J. Lin and C. Hsu, “A simple decomposition method for support vector machine,” Machine Learning, vol. 46, no. 1-3, pp. 219–314, 2002.
• [7] T. Travelling Salesman Problem, n.d.
• [8] J. Wei, “Approaches to the Travelling Salesman Problem Using Evolutionary Computing Algorithms,” 2008.