A Markov Chain analysis of genetic algorithms with power of 2 cardinality alphabets |
| |
| Institution: | 1. Endocrine and Metabolic Unit, Royal Adelaide Hospital, Adelaide, South Australia, Australia;2. School of Medicine, University of Adelaide, Adelaide, South Australia, Australia;3. Department of Thoracic Medicine, Royal Adelaide Hospital, Adelaide, South Australia, Australia;4. Chemical Pathology Directorate, SA Pathology, Adelaide, South Australia, Australia;5. Steroid & Immunobiochemistry Laboratory, Canterbury Health Laboratories, Christchurch, New Zealand;1. Division of Gastroenterology and Hepatology, E-Da Hospital/I-Shou University, Kaohsiung, Taiwan;2. Division of Nephrology, Department of Internal Medicine, E-Da Hospital/I-Shou University, Kaohsiung, Taiwan;3. School of Medicine, I-Shou University, Kaohsiung, Taiwan;4. Division of Gastroenterology and Hepatology, Department of Internal Medicine, E-Da Cancer Hospital/I-Shou University, Kaohsiung, Taiwan;5. Division of Gastroenterology, Yuan''s General Hospital, Kaohsiung, Taiwan;6. Center for Liver Diseases, E-Da Hospital/I-Shou University, Kaohsiung, Taiwan;7. Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan. |
| |
| Abstract: | In this paper we model the run time behavior of GAs using higher cardinality representations as Markov chains, define the states of the Markov Chain and derive the transition probabilities of the corresponding transition matrix. We analyze the behavior of this chain and obtain bounds on its convergence rate and bounds on the runtime complexity of the GA. We further investigate the effects of using binary versus higher cardinality representation of a search space. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
