Name
Abstract | ||
|---|---|---|
Combinatorial optimization problems form a class of appealing theoretical and practical problems attractive for their complexity and known hardness. They are often NP-hard and as such not solvable by exact methods. Combinatorial optimization problems are subject to numerous heuristic and metaheuristic algorithms, including genetic algorithms. In this paper, we present two new permutation encodings for genetic algorithms and experimentally evaluate the influence of the encodings on the performance and result of genetic algorithm on two synthetic and real-world optimization problems. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/SoCPaR.2009.31 | SoCPaR |
Keywords | Field | DocType |
genetic algorithms,numerous heuristic,new permutation encodings,modeling permutations,exact method,genetic algorithm,combinatorial optimization problem,practical problem,real-world optimization problem,metaheuristic algorithm,gallium,computational complexity,genetics,np hard problem,optimization problem,heuristic algorithm,permutation,optimization,encoding | Mathematical optimization,Heuristic (computer science),Computer science,Meta-optimization,L-reduction,Quality control and genetic algorithms,Optimization problem,Genetic algorithm,Metaheuristic,Computational complexity theory | Conference |
Citations | PageRank | References |
2 | 0.39 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Krömer Pavel | 1 | 330 | 59.99 |
| Jan Plato | 2 | 17 | 3.30 |
| Václav Snáel | 3 | 37 | 10.63 |