Abstract
Genetic Algorithms (GA) is an evolutionary inspired heuristic search algorithm. Like all heuristic search methods, the probability of locating the optimal solution is not unity. Therefore, this reduces GA's usefulness in areas that require reliable and accurate optimal solutions, such as in system modeling and control gain setting. In this paper an alteration to Genetic Algorithms (GA) is presented. This method is designed to create a specific type of diversity in order to obtain more optimal results. In particular, it is done by mutating bits that are not constant within the population. The resultant diversity and final optimality for this method is compared with standard Mutation at various probabilities. Simulation results show that this method improves search optimality for certain types of problems.
| Original language | English |
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| Title of host publication | Artificial Intelligence and Computational Intelligence: International Conference, AICI 2009, Shanghai, China, November 7-8, 2009: Proceedings |
| Editors | Hepu Deng, Lanzhou Wang, Fu Lee Wang, Jingsheng Lei |
| Place of Publication | Germany |
| Publisher | Springer |
| ISBN (Print) | 9783642052521 |
| Publication status | Published - 2009 |