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2003

Sample Complexity of Real-Coded Evolutionary Algorithms

13 years 5 months ago
Sample Complexity of Real-Coded Evolutionary Algorithms
Researchers studying Evolutionary Algorithms and their applications have always been confronted with the sample complexity problem. The relationship between population size and global convergence is not clearly understood. Population size is usually chosen depending on researcher’s experience. In this paper, we study the population size using Probably Approximately Correct (PAC) learning theory. A ruggedness measure for fitness functions is defined. A sampling theorem that theoretically determines an appropriate population size towards effective convergence is proposed. Preliminary experiments show that the initial population of the proposed size provides good starting point(s) for searching the solution space and thus leads to finding global optima.
Jian Zhang 0007, Xiaohui Yuan, Bill P. Buckles
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where FLAIRS
Authors Jian Zhang 0007, Xiaohui Yuan, Bill P. Buckles
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