An adaptive restarting genetic algorithm for global optimization
Date
2015
Authors
Dao, S.D.
Abhary, K.
Marian, R.
Editors
Ao, S.I.
Douglas, C.
Grundfest, W.S.
Burgstone, J.
Douglas, C.
Grundfest, W.S.
Burgstone, J.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Lecture Notes in Engineering and Computer Science, 2015 / Ao, S.I., Douglas, C., Grundfest, W.S., Burgstone, J. (ed./s), vol.1, pp.455-459
Statement of Responsibility
Conference Name
World Congress on Engineering and Computer Science (21 Oct 2015 - 23 Oct 2015 : San Francisco, US)
Abstract
Genetic Algorithm (GA) is a popular stochastic optimization technique, often used to solve complex large scale optimization problems in many fields. Enhancing the search capability of GA is always desirable. In this paper, an innovative GA, called adaptive restarting GA, is developed to improve the global search capability of the algorithm. With an adaptive restarting procedure and elite chromosome strategy, the proposed GA is capable of jumping out of the local optima and finding the global optimum with very high success probability. Two benchmark functions are used to demonstrate the outperformance of the proposed GA, in comparison with five other algorithms available in global optimization literature, including the traditional GA
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Dissertation Note
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Copyright 2015 International Association of Engineers