Name
Abstract | ||
|---|---|---|
The ordinary genetic algorithm may be thought of as conducting a single market in which solutions compete for success, as mea- sured by the fitness funtion. We introduce a two-market genetic algorithm, consisting of two phases, each of which is an ordinary single-market genetic algorithm. The two- market genetic algorithm has a natural inter- pretation as a method of solving constrained optimization problems. Phase 1 is optimality improvement; it works on the problem with- out regard to constraints. Phase 2 is feasi- bility improvement; it works on the existing population of solutions and drives it towards feasibility. We tested this concept on 14 stan- dard knapsack test problems for genetic al- gorithms, with excellent results. The paper concludes with discussions of why the two- market genetic algorithm is successful and of how this work can be extended. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-45105-6_123 | GECCO |
Keywords | Field | DocType |
two-market genetic algorithm,genetic algorithm | Genetic operator,Mathematical optimization,Computer science,Meta-optimization,Genetic representation,Cultural algorithm,Knapsack problem,Population-based incremental learning,Quality control and genetic algorithms,Genetic algorithm | Conference |
Volume | ISSN | ISBN |
2723 | 0302-9743 | 1-55860-878-8 |
Citations | PageRank | References |
17 | 1.53 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven O. Kimbrough | 1 | 600 | 103.93 |
| Ming Lu | 2 | 65 | 4.94 |
| david harlan wood | 3 | 168 | 19.69 |
| Dong-Jun Wu | 4 | 23 | 3.76 |