24th Jan, 2020

Islamic Azad University Tehran Science and Research Branch

Question

Asked 19th Aug, 2016

Computing time complexity of Genetic Algorithm

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The following paper would be useful

the following paper would be useful

"Time Complexity Analysis of the Genetic Algorithm Clustering Method" by NOPIAH et al.

Hojjat Allah Bazoobandi answer is very correct

5 Recommendations

The most time consuming part in an Evolutionary Algorithm(EA) is the Fitness Function. Therefore, "Number of Fitness Function Evaluations" usually used as performance criterion in EAs.

However, if you insist to evaluate the time complexity of EAs with Big O notation, you can use O(NG), where N describe the size of population and G stands for number of iterations.

2 Recommendations

I totally agree with Mr.Lago. Complexity at very basic level is the count of no of times a basic operation is executed. We are also clear of the fact that, basic operations will always lurk inside a nested iterative structure. Therefore, in this case it may be taken as no. of generations + no of cross overs + no of mutations which leads to an expression. Consider only higher order term in the so obtained expression and this surely forms a key for big O notation.

I am a person who would disagree with all the above answers. First of all, a non-deterministic method approximate a solution. Perhaps an optimum is found, perhaps a near optimum, perhaps a very bad solution. Nothing is guaranteed and therefore the big O notation may just analyse an algorithm that does not work.

Article

- Aug 2005

The information potential of microartefacts can assist in archaeological interpretation. In this paper, a method for estimating the weight of microartefacts is presented. This method is based on Genetic Algorithms, a stochastic minimisation method that comes from Computational Intelligence techniques. It was observed that Genetic Algorithms offer a...

Data

- Jan 2012

Article

Full-text available

Combining different Computational Intelligence techniques has obtained significant results in several areas. Genetic algorithms combined with fuzzy systems present advantages over their isolated utilization. In this paper a genetic-fuzzy system applied to the medical area is proposed. Two different approaches are implemented and compared. The propo...

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