TY - GEN
T1 - Genetic Algorithm-based ecosystem for heather management
AU - Jin, Nanlin
PY - 2008
Y1 - 2008
N2 - This paper applies Genetic Algorithms (GA) to simulate a heather moorland ecosystem. We investigate, in this ecosystem how to manage heather for the benefits of survival and reproduction of grouse. A GA candidate solution is a grid, representing spatial relationship of three types of heather. From solutions provided by GA, we have found that the diversity of neighborhood and its distribution are essential. The evenly diversified heather distributions emerge as the best fit solutions for grouse's needs. We compared this finding with data collected from the field work.
AB - This paper applies Genetic Algorithms (GA) to simulate a heather moorland ecosystem. We investigate, in this ecosystem how to manage heather for the benefits of survival and reproduction of grouse. A GA candidate solution is a grid, representing spatial relationship of three types of heather. From solutions provided by GA, we have found that the diversity of neighborhood and its distribution are essential. The evenly diversified heather distributions emerge as the best fit solutions for grouse's needs. We compared this finding with data collected from the field work.
UR - http://www.scopus.com/inward/record.url?scp=55749113826&partnerID=8YFLogxK
U2 - 10.1109/CEC.2008.4631242
DO - 10.1109/CEC.2008.4631242
M3 - Conference Proceeding
AN - SCOPUS:55749113826
SN - 9781424418237
T3 - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
SP - 3282
EP - 3288
BT - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
T2 - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
Y2 - 1 June 2008 through 6 June 2008
ER -