TY - CONF
T1 - Multi-population adapative inflationary differential evolution
AU - Di Carlo, Marilena
AU - Vasile, Massimiliano
AU - Minisci, Edmondo
PY - 2014/9/13
Y1 - 2014/9/13
N2 - 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.
AB - 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.
KW - adaptive algorithms
KW - differential evolution
KW - global optimization
UR - http://bioma.ijs.si/conference/2014/
M3 - Paper
SP - 41
EP - 54
T2 - Bio-inspired Optimization Methods and their Applications, BIOMA 14
Y2 - 13 September 2014 through 13 September 2014
ER -