Genetic Algorithm : Lower and Upper Bounds

How to choose the VarMin (Lower Bound of decision Variables) and VarMax (Upper Bound of Variables) of any problem when using the genetic algorithm optimization ? What its effect on improvement ?

Answer 1

[Stephan]

Hi,

the handling of bounds in the case of the use of ga is very well described here:

https://de.mathworks.com/help/gads/constraints.html

Essentially, ga ensures that the initial population complies with the restrictions on bounds. Another point is compliance with the restrictions over the term of the algorithm, if certain conditions are met.

To define them use for example:

lb = [-1 0 0 -inf];
ub = [Inf 1 1 0];

In the example above you have 4 decision variables x1...x4 which solutions are in the defined ranges of lb (1)...ub (1) for x1 and so on.

Determining meaningful bounds depends on the problem and requires insight into the problem. Possible limitations may arise from physical or economic considerations. if you have normalized decision variables, they will be between 0...1. You can not produce -5 cars, the optimal power can not exceed the maximum power that is possible with the used drive...

So the effect on improvement by using bounds is getting feasible solutions to a problem on the one hand. On the other hand it is reducing the search space, which helps the solver finding good solutions faster.

Best regards

Stephan

Answer 2

[Husnain Raees]

matlab code for atrifical bee coloney with uper and lower bounds

Answer 3

[Prathamesh]

perform inveres kinematics to formulate the equation oe x, y,z co ordinate of SCARA robot

l1=150mm, l2=225 mm,l3=175mm, l4=10mm

x=-25mm,y-270mm,z=150mm

bounds =-132<X1<132, -141<X2<141, 0<d<-50