Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Based Distribution Service Restoration with Minimum Average Energy Not Supplied
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Group search optimizer: an optimization algorithm inspired by animal searching behavior
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
This paper presents the key cutting algorithm and its variants. This algorithm emulates the work of locksmiths to defeat the lock. The best key that matches a given lock is pretended to be an optimal solution of a relevant optimization problem. The basic structure of the key cutting algorithm is as simple as that of genetic algorithms in which a string of binary numbers is employed as a key to open the lock. In this paper, four variants of the predecessor are proposed. The modification is mainly in the key cutting selection. Various criteria of the key cutting probability are added in order to improve the searching speed and the solution convergence. To evaluate their use, four standard test functions are challenged and therefore which satisfactory best solutions obtained from the key cutting variants are compared with those obtained from genetic algorithms. The results confirm the effectiveness of the key cutting and its variants to solve the unconstrained optimization problems.