Convergence analysis of quantum-inspired genetic algorithms with the population of a single individual

In this paper, the Quantum-inspired Genetic Algorithms with the population of a single individual are formalized by a Markov chain model using a single and the stored best individual. Here, we analyze the convergence property of the Quantum-inspired Genetic Algorithms based on our proposed mathematical model, and with assumption in which its special genetic operation in the generation changes is restricted to a quantum operator; and show by means of the Markov chain analysis that the algorithm with preservation of the best individual in the population and comparison of it with the existing individual, will converge on the global optimal solution. Categories and Subject Descriptors: I.2.3 [Artificial Intelligence]: Deduction and Theorem Proving – deduction G.3 [Mathematics of Computing]: Probability and Statistics – Markov Processes General Terms: Verification

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Mehrshad Khosraviani, Saadat Pour-Mozafari, Mohammad Mehdi Ebadzadeh
• Keywords: GECCO 2008, Markov Chain, Markov Chain Model, Optimization, Quantum-inspired Genetic Algorithms