Biomedical Engineering Reference
In-Depth Information
maximum number of generations has been produced, or a satisfactory fitness level
has been reached for the population [
8
]. The various steps in GA-based optimi-
zation are detailed below:
Initialization
From the initial population few individual solutions are generated. The population
is generated randomly, covering the entire range of possible solutions.
Selection
In each generation, individual solutions are selected by evaluating the fitness
function and the fitter solutions have a higher probability of selection.
Reproduction
Next set of population for the successive generation is generated by a process
called reproduction and involves crossover (recombination) and mutation. These
results in a new set of population derived from the fitter solutions of the previous
population. Generally the average fitness of the population is heightened as
compared to the population of the previous population.
Termination
The process of optimization is halted once a termination condition is achieved. The
termination condition can be either the number of generations or the solution
satisfying an optimum criterion [
9
].
Implementation of GA Controller
GA can be applied to the tuning of PID position controller gains to ensure optimal
control
performance
at
nominal
operating
conditions
[
10
,
11
].
The
Genetic
Algorithm parameters chosen for the tuning purpose (Table
3
, Fig.
5
).
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