Name
Abstract | ||
|---|---|---|
Genetic Programming (GP) is one of the successful evolutionary computation techniques applied to solve classification problems, by searching for the best classification model applying the fitness evaluation. The fitness evaluation process greatly impacts the overall execution time of GP and is therefore the focus of this research study. This paper proposes a segment-based GP (SegGP) technique that reduces the execution time of GP by partitioning the dataset into segments, and using the segments in the fitness evaluation process. Experiments were done using four datasets and the results show that SegGP can obtain higher or similar accuracy results in shorter execution time compared to standard GP. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2464576.2464648 | GECCO (Companion) |
Keywords | Field | DocType |
genetic programming,execution time,segment-based genetic programming,fitness evaluation,overall execution time,standard gp,best classification model,fitness evaluation process,classification problem,segment-based gp,shorter execution time,evolutionary computation | Interactive evolutionary computation,Computer science,Evolutionary computation,Genetic programming,Fitness approximation,Artificial intelligence,Execution time,Data classification,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nailah AL-Madi | 1 | 36 | 3.76 |
| Simone A Ludwig | 2 | 1309 | 179.41 |