A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
ASPARAGOS an asynchronous parallel genetic optimization strategy
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
The Breeder Genetic Algorithm for Frequency Assignment
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Permutation Based Genetic Algorithm for Minimum Span Frequency Assignment
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Evolutionary divide and conquer (i): A novel genetic approach to the tsp
Evolutionary Computation
A genetic algorithm for frequency assignment with problem decomposition
International Journal of Mobile Network Design and Innovation
Computers and Operations Research
New order-based crossovers for the graph coloring problem
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A wide-ranging computational comparison of high-performance graph colouring algorithms
Computers and Operations Research
| Hi-index | 0.00 |
The genetic algorithm (GA) described in this paper breeds permutations of transmitters for minimum span frequency assignment. The approach hybridizes a GA with a greedy algorithm, and employs a technique called Generalized Saturation Degree to seed the initial population. Several permutation operators from the GA literature are compared, and results indicate that position based operators are more appropriate for this kind of problem than are order based operators. My offspring versus mid-parent correlation studies on crossovers show Pearson's correlation coefficient to be a reliable predictor of performance in most cases. Results presented herein represent improvements over previously published results.