Name
Abstract | ||
|---|---|---|
This work presents a unique genetic programming (GP) approach that integrates a numerical local search method and a bloat-control mechanism to address some of the main issues with traditional GP. The former provides a directed search operator to work in conjunction with standard syntax operators that perform more exploration in design space, while the latter controls code growth by maintaining program diversity through speciation. The system can produce highly parsimonious solutions, thus reducing the cost of performing the local optimization process. The proposal is extensively evaluated using real-world problems from diverse domains, and the behavior of the search is analyzed from several different perspectives, including how species evolve, the effect of the local search process and the interpretability of the results. Results show that the proposed approach compares favorably with a standard approach, and that the hybrid algorithm can be used as a viable alternative for solving real-world symbolic regression problems. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10710-019-09351-7 | Genetic Programming and Evolvable Machines |
Keywords | Field | DocType |
Genetic programming, Bloat, NEAT, Local search | Design space,Interpretability,Hybrid algorithm,Computer science,Genetic programming,Operator (computer programming),Artificial intelligence,Local search (optimization),Syntax,Symbolic regression,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 3 | 1389-2576 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Perla Juárez-Smith | 1 | 1 | 0.68 |
| Leonardo Trujillo | 2 | 41 | 11.33 |
| Mario García Valdez | 3 | 304 | 26.97 |
| Francisco Fernandéz | 4 | 58 | 9.36 |
| Francisco Chávez | 5 | 41 | 9.22 |