Name
Abstract | ||
|---|---|---|
For evolutionary algorithms (EAs), selection is one of the main components which decides solutions for the new population. Most selection strategies are fitness-based and prodigal in fitness evaluations, since many evaluated solutions are discarded immediately due to their worse values. It is desirable to predict the quality of new solutions without the evaluations before selection, thus the efficiency of EAs can be improved. Naturally selection can be considered as a classification problem: selected solutions belong to the 'good' class and the discarded ones belong to the 'bad' class. This paper demonstrates this idea by introducing a classification-based selection (CBS) strategy for EAs. The CBS is integrated into a state-of-the-art algorithm and studied on a test suite. The experimental results evidence the efficiency of CBS on saving the number of fitness evaluations when compared with the original algorithm.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3319619.3322077 | GECCO |
Keywords | Field | DocType |
Classification, selection, evolutionary algorithms | Computer science,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6748-6 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinyuan Zhang | 1 | 0 | 0.34 |
| Xiangji Huang | 2 | 1551 | 159.34 |
| Qinmin Vivian Hu | 3 | 20 | 6.06 |