Name
Abstract | ||
|---|---|---|
Genetic algorithms (GAs) are powerful tools for solving complex optimization problems, usually using a haploid representation. In the past decades, there has been a growing interest concerning the diploid genetic algorithms. Even though this area seems to be attractive, it lacks wider coverage and research in the Evolutionary Computation community. The scope of this paper is to provide some reasons why this situation happens and in order to fulfill this aim, we present experimental results using a conventional haploid GA and a developed diploid GA tested on some major benchmark functions used for performance evaluation of genetic algorithms. The obtained results show the superiority of the diploid GA over the conventional haploid GA in the case of the considered benchmark functions. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-29859-3_17 | HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2019 |
Keywords | Field | DocType |
Haploid and diploid genetic algorithms, Benchmark functions, Comparative study | Ploidy,Computer science,Evolutionary computation,Theoretical computer science,Artificial intelligence,Optimization problem,Genetic algorithm,Machine learning | Conference |
Volume | ISSN | Citations |
11734 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrian Petrovan | 1 | 0 | 0.34 |
| Petrica Pop Sitar | 2 | 0 | 0.68 |
| Oliviu Matei | 3 | 43 | 11.15 |