Research Output per year
In this paper, a multi-population version of Adaptive Inflationary Differential Evolution, which automatically adapts the crossover probability and the differential weight of the Differential Evolution, is presented. The multi-population algorithm exploits the use of different populations, and the local minima found by each population, to assess the distance between minima; a probabilistic kernel based approach is then used to automatically adapt the dimension of a bubble in which the population is re-initialized after converging to a local minimum. The algorithm is tested on two real case functions and on two difficult academic functions.
|Publication status||Published - 13 Sep 2014|
|Event||Bio-inspired Optimization Methods and their Applications, BIOMA 14 - Ljubljana, Slovenia|
Duration: 13 Sep 2014 → 13 Sep 2014
|Conference||Bio-inspired Optimization Methods and their Applications, BIOMA 14|
|Period||13/09/14 → 13/09/14|
- adaptive algorithms
- differential evolution
- global optimization
Vasile, M. & Minisci, E., 15 Jul 2019, In : Soft Computing. 31 p.
Research output: Contribution to journal › Article
Minisci, E. & Riccardi, A., 1 Jun 2018, In : Journal of Physics: Conference Series . 1037, 4, 10 p., 042011.
Research output: Contribution to journal › Conference Contribution
Di Carlo, M., Vasile, M., & Minisci, E. (2014). Multi-population adapative inflationary differential evolution. 41-54. Paper presented at Bio-inspired Optimization Methods and their Applications, BIOMA 14, Ljubljana, Slovenia.