Ho to Constraint GA to Generate Population Within Design Space

5 次查看（过去 30 天）
Mirhan
Mirhan 2025-4-10
编辑： Matt J 2025-4-10
Dear all,
I’m currently working on a genetic algorithm-based optimization and would like to constrain the GA to select design variables strictly within a predefined design space.
While I can set upper and lower bounds for the variables, my design space is more complex, and I’m working with a large population. I’m wondering if there’s a way to configure the GA so that it generates the populations entirely within the valid design space, rather than just filtering or penalizing out-of-bounds individuals afterward.
Attached is a simple sketch illustrating the shape of my design space.
Thanks in advance for your guidance!
Best regards,
Mirhan

请先登录，再回答此问题。

采纳的回答

Matt J
Matt J 2025-4-10
编辑：Matt J 2025-4-10
Yes, you can reparametrize the design vector x as,
x=t1*v1+t2*v2
where v1 and v2 are the direction vectors of the two rays bounding your cone,
v1=[cosd(150),sind(150)]
v2=[cosd(30), sind(30)]
The new design variables are t=[t1,t2], which only need simple positivity constraints to confine x to the cone.
  2 个评论
Mirhan
Mirhan 2025-4-10
Thank you very much for your quick and helpful response!
I was wondering—how can I implement this into a classical genetic algorithm? I'm not quite sure where exactly this type of constraint should be defined within the algorithm.
Thank you again!
Best regards,
Mirhan
LB = [0 0 0];
UB = [10 10 10];
%% Optimization Options
PopulationSize = 50*nvars;
Generations = 50;
% Set GA options
options = optimoptions( 'ga',...
'Display', 'iter',...
'TolFun', 1e-9,...
'TolCon', 1e-3,...
'PlotFcn',{@gaplotbestf,@gaplotdistance});
ObjectiveFunction = @(x) simple_objective (x);
% Run Genetic Algorithm
[x_opt, fval_opt] = ga(ObjectiveFunction, nvars, [], [], [], [], LB, UB, [], options);
Matt J
Matt J 2025-4-10
编辑：Matt J 2025-4-10
The code from your last comment is for a 3D problem, whereas your diagram was for a 2D problem. I will continue to assume the 2D problem whose design space is a cone at 30 degrees to the x-axis.
You need to reparametrize your fitness function in terms of t,
xoft=@(t) t(1)*v(1)+t(2)*v(2);
ObjectiveFunction = @(t) simple_objective( xoft(t));
% Run Genetic Algorithm
[t_opt, fval_opt] = ga(ObjectiveFunction, 2, [], [], [], [], [0,0], [], [], options);
x_opt = xoft(t_opt)

请先登录，再进行评论。

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Genetic Algorithm 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by