Name
Abstract | ||
|---|---|---|
Evolution strategies have been successfully applied to optimization problems with rugged, multi-modal fitness landscapes, to non linear problems, and to derivative free optimization. Usually evolution is performed by exploiting the structure of the objective function. In this paper, we present an approach that harnesses the adapting quantum potential field determined by the spatial distribution of elitist solutions as guidance for the next generation. The potential field evolves to a smoother surface leveling local optima but keeping the global structure what in turn allows for a faster convergence of the solution set. We demonstrate the applicability and the competitiveness of our approach compared with particle swarm optimization and the well established evolution strategy CMA-ES. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5220/0006037000210029 | PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE, VOL 1: ECTA |
Keywords | Field | DocType |
Evolution Strategies, Global Optimization, Surrogate Optimization, Quantum Potential | Mathematical optimization,Computer science,Quantum field theory,Evolution strategy | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jörg Bremer | 1 | 34 | 13.32 |
| Lehnhoff, S. | 2 | 40 | 5.89 |