Bio-medical Ontologies Maintenance and Change Management

Genetic Algorithm in Ab Initio Protein Structure Prediction 333
Total Fitness = α ( t)
× F + β ( t)
× PCF
(2)
where t is t th generation while search is carried out by the GA. To adjust the
weights α and β to dominate F and PCF over each other, the oscillatory func-
tion δ (t)
shown in Fig. 19, is introduced. The setup maintains a variation in the
amplitude (A).
where ω m 0
ω 0
(4)
Phase 2: α( t) = 1,
β ( t)
= −δ
( t)
, when δ ( t)
< 0
(5)
Transient Phase: α ( t) : = 1,
β ( t)
: = 1,
when δ ( t)
= 0
(6)
Typical parameter values for the δ (t)
function (see plot in Fig. 19) were set as
follows: A = 30, ω m = 0.004 and ω 0 = 0.05. The choice of A came from
2A ≥ max ( Fl
, PCFl
) where l F and PCF l respectively imply the upper bounds
of F and PCF, which is predictable from the chosen model. The lower bound of F
can be defined by (7) for 2D square and 3D cube HP lattice model and (8) for 2D
FCC and 3D FCC model.
F l = −2
* dim* (min{ E[
Seq],
O[
Seq]})
+ n
(7)
( × n n )
TH
(3)
l = − dim H TH
(8)
F +
where in (7), E[Seq] and O [Seq]
indicate the number of even and odd indexed H
residues in the sequence and nT indicates number of terminal H residue, where
H