Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

a
= ∑ Lt (π 0 P n ) i
i=1 n→∝
a
= ∑ Lt (π 0 . I )i
i=1 n→∝
(I – Q) – 1 R
= 1.
15.5 Application of GA in
Optimization Problems
Genetic algorithms have been successfully used in many optimization
problems. For instance, the classical travelling salesperson problem, the
flowshop and the jobshop scheduling problems and many of the constraint
satisfaction problems can be handled with GA. In this section, we illustrate the
use of GA in antenna design. There exist various types of antennas. We here
illustrate the design of a specialized antenna, called a monopole loaded with a
folded dipole. The whole antenna has 6 components (fig. 15.6), namely Z1, Z2,
Z3, Z4, X1 and X2. We want the electric field vector E θ to be optimized in one
half of the hemisphere. A non-specialist reader can easily understand the
meaning of E θ from fig. 15.7.
Altshuler and Linden [1] used an NEC2 package that computes Eθ for
given arm-lengths of the antenna (fig. 15.8). The GA program, used by them,
on the other hand evolves new chromosomes with 6 fields vide fig. 15.9. Each
field is represented by 5 bits, thus having a possible length in the range of 0
and 31 units. The selection criterion in the present context [1] is given by
Minimize Z = ∑ ( E θ -Edesired ) 2 .
-180 ≤ ∀ϕ ≤ 0