Computational Complexity and Genetic Algorithms
نویسندگان
چکیده
Recent theory work has suggested that the genetic algorithm (GA) complexity of a problem can be measured by the growth rate of the minimum problem representation [1]. This paper describes a method for evaluating the computational complexity of a problem specifically for a GA. In particular, we show that the GAcomplexity of a problem is determined by the growth rate of the minimum representation as the size of the problem instance increases. This measurement is then applied to evaluate the GA-complexity of two dissimilar problems. Once the GA-complexities of the problems are known, they are then compared to what is already known about the problems' complexities. These results lead to the definition of a new complexity class called "NPG". Future directions for research are then proposed. Key-Words: Complexity, Genetic, Algorithm, GA-complexity
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