Name
Abstract | ||
|---|---|---|
Efficient discovery of lowest level building blocks is a fundamental requirement for a successful genetic algorithm. Although considerable effort has been directed at techniques for combining existing building blocks there has been little emphasis placed on discovering those blocks in the first place. This paper describes an analysis of the canonical genetic algorithm that demonstrates a significant weakness in the algorithm and suggests that careful use of random search will lead to better performance than the use of mutation. Experimental results show that this can result in significant performance improvements over the canonical genetic algorithm. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CEC.2007.4424796 | 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS |
Keywords | Field | DocType |
genetic algorithms,genetic algorithm,random search | Genetic operator,Mathematical optimization,Search algorithm,Computer science,Meta-optimization,Genetic representation,Artificial intelligence,Cultural algorithm,Population-based incremental learning,Genetic algorithm,Machine learning,Best-first search | Conference |
Citations | PageRank | References |
1 | 0.36 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cameron Skinner | 1 | 18 | 2.63 |
| Patricia J. Riddle | 2 | 202 | 22.84 |