Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Multiple Vehicle Routing with Time and Capacity Constraints Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Network random keys: a tree representation scheme for genetic and evolutionary algorithms
Evolutionary Computation
| Hi-index | 0.00 |
This paper presents a scaling analysis of the ordering messy genetic algorithm (OmeGA), a fast messy genetic algorithm that uses random keys to represent solutions. In experiments with hard permutation problems--so-called ordering deceptive problems--it is shown that the algorithm scales up as O(l1.4) with the problem length l ranging from 32 to 512. Moreover, the OmeGA performs efficiently with small populations thereby consuming little memory. Since the algorithm is independent of the structure of the building blocks, it outperforms the random key-based simple genetic algorithm (RKGA) for loosely coded problems.