A constraint-guided method with evolutionary algorithms for economic problems |
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| Authors: | Nanlin Jin Edward Tsang Jin Li |
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| Affiliation: | 1. University of Leeds, School of Geography, Leeds LS2 9JT, UK;2. University of Essex, School of Computer Science and Electronic Engineering, Colchester CO4 3SQ, UK;3. Unilever, UK;1. Center for Proteomics and Department of Nutrition, Case Western Reserve University, Cleveland, OH, USA;2. Alan Edwards Centre for Research on Pain, McGill University, Montreal, Quebec, Canada;1. School of Electronics and Information, Northwestern Polytechnical University, Shaanxi 710129, China;2. Department of Computer Science, The University of North Carolina at Charlotte, NC28223, United States;1. Department of Microelectronics, Brno University of Technology, Technická 10, Brno, Czech Republic;2. Faculty of Biomedical Engineering, Czech Technical University in Prague, nám. Sítná 3105, Kladno, Czech Republic;3. Faculty of Engineering, King Mongkut''s Institute of Technology Ladkrabang, Bangkok 10520, Thailand;4. Department of Electrical Engineering, Technical University of Cz?stochowa, 42-201 Cz?stochowa, Poland |
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| Abstract: | This paper presents an evolutionary algorithms based constrain-guided method (CGM) that is capable of handling both hard and soft constraints in optimization problems. While searching for constraint-satisfied solutions, the method differentiates candidate solutions by assigning them with different fitness values, enabling favorite solutions to be distinguished more likely and more effectively from unfavored ones.We illustrate the use of CGM in solving two economic problems with optimization involved: (1) searching equilibriums for bargaining problems; (2) reducing the rate of failure in financial prediction problems. The efficacy of the proposed CGM is analyzed and compared with some other computational techniques, including a repair method and a penalty method for the problem (1), a linear classifier and three neural networks for the problem (2), respectively. Our studies here suggest that the evolutionary algorithms based CGM compares favorably against those computational approaches. |
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