Name
Abstract | ||
|---|---|---|
The paper describes the design of an efficient and robust genetic algorithm for the nuclear fuel loading problem (i.e. refuellings: the in-core fuel management problem) - a complex combinatorial, multimodal optimisation. Evolutionary computation as performed by FUELGEN replaces heuristic search of the kind performed by the FUELCON expert system (CAI 12/4), to solve the same problem. In contrast to the traditional genetic algorithm which makes strong requirements on the representation used and its parameter settings in order to be efficient, the results of recent research on new, robust genetic algorithms show that representations unsuitable for the traditional genetic algorithm carl still be used to good effect with little parameter adjustment.The representation presented here is a simple symbolic one with no linkage attributes, making the genetic algorithm particularly easy to apply to fuel loading problems with differing core structures and assembly inventories. A nonlinear fitness function has been constructed to direct the search efficiently in the presence of the many local optima that result from the constraint on solutions. |
Year | Venue | Field |
|---|---|---|
1998 | COMPUTERS AND ARTIFICIAL INTELLIGENCE | Heuristic,Nonlinear system,Local optimum,Computer science,Expert system,Algorithm,Evolutionary computation,Fitness function,Genetic representation,Genetic algorithm |
DocType | Volume | Issue |
Journal | 17 | 2-3 |
ISSN | Citations | PageRank |
0232-0274 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Zhao | 1 | 0 | 0.34 |
| Brian Knight | 2 | 148 | 22.62 |
| Ephraim Nissan | 3 | 164 | 21.59 |
| Alan Soper | 4 | 0 | 0.68 |