Name
Abstract | ||
|---|---|---|
ABSTRACTThis paper presents a framework for systematically investigating and designing fuzzy rulesets for Adaptive Fuzzy Particle Swarm Optimization (AFPSO) algorithms. Training is achieved through Gaussian Process (GP) supported by Gradient Boosted Regression Trees (GBRT). Meta-objective was defined by ranks on various benchmark functions. Validation benchmarks were also performed on GECCO '20 bound-constrained optimization competition. The resulting variants, particularly those controlling hybridization with Quantum Particle Swarm Optimization (QPSO) surpassed classical Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE) on the training functions. Some level of generalization was also observed on most of the validation set. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3449726.3459418 | Genetic and Evolutionary Computation Conference |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Roy | 1 | 0 | 0.34 |
| Charlotte Beauthier | 2 | 1 | 2.08 |
| T Carletti | 3 | 37 | 14.43 |
| Alexandre Mayer | 4 | 0 | 1.01 |