< prev

Page 1Page 2Page 3Page 4Page 5Page 6Page 7Page 8Page 9Page 10Page 11Page 12

Page 3 of 12
next >

Majalah Ilmiah UNIKOM

Vol.8, No. 1

3

H a l a m a n

crossover), and their "fitness" values are

computed. Then a new generation of the

population is constructed by selection from

newly generated and old individuals. Figure

2 shows the pseudocode of GA.

There is some genetic algorithm terminol-

ogy:

 Fitness Functions : the function we want

to optimize. For standard optimization

algorithms, this is known as the objective

function.

 Individuals : is any point to which you can

apply the fitness function. The value of

the fitness function for an individual is its

score. An individual is sometimes re-

ferred to as a genome and the vector

entries of an individual as genes.

 Populations: is an array of individuals.

 Generations. At each iteration, the ge-

netic algorithm performs a series of com-

putations on the current population to

Muhammad Aria

Figure 1. Pseudocode of Simulated Annealing

Figure 2. Pseudocode of Genetic Algorithm