- author = "Nanlin Jin and Edward Tsang and Jin Li",
- title = "A constraint-guided method with evolutionary algorithms for economic problems",
- journal = "Applied Soft Computing",
- volume = "9",
- number = "3",
- pages = "924--935",
- year = "2009",
- ISSN = "1568-4946",
-
DOI = "
doi:10.1016/j.asoc.2008.11.006",
-
URL = "
http://www.sciencedirect.com/science/article/B6W86-4V0TCY0-6/2/6b82133b94fa2c3580d4e43064120400",
- keywords = "genetic algorithms, genetic programming, Constraint satisfaction, Economic problems",
-
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 unfavoured
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 analysed 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.",
