Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.asoc.2008.11.006 | Appl. Soft Comput. |
Keywords | Field | DocType |
method differentiates candidate solution,genetic programming,constraint-guided method,economic problems,constraint satisfaction genetic programming economic problems,evolutionary algorithm,proposed cgm,computational technique,computational approach,constrain-guided method,penalty method,constraint satisfaction,repair method,economic problem,bargaining problem,optimization problem,neural network,satisfiability | Constraint satisfaction,Mathematical optimization,Evolutionary algorithm,Computer science,Genetic programming,Artificial intelligence,Linear classifier,Artificial neural network,Economic problem,Optimization problem,Machine learning,Penalty method | Journal |
Volume | Issue | ISSN |
9 | 3 | Applied Soft Computing Journal |
Citations | PageRank | References |
7 | 0.58 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nanlin Jin | 1 | 51 | 8.70 |
| Edward P. K. Tsang | 2 | 899 | 87.77 |
| Jin Li | 3 | 7 | 0.58 |