Minimizing with differential evolution












4












$begingroup$


A differential evolution algorithm is given here. I would like to get this kind of animation. I thought I could use NMinimize, given
DifferentialEvolution as an option, but it turns out that does not work as I espected.



Is it possible to extract intermediate step in DifferentialEvolution, or do I have to implement algorithm myself?



f[x_, y_] := 
-20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) - E^(0.5 (Cos[2 π x] + Cos[2 π y])) + E + 20

p1 =
Plot3D[f[x, y], {x, -5, 5}, {y, -5, 5},
PerformanceGoal -> "Quality",
ColorFunction -> "WatermelonColors",
Mesh -> None,
BoxRatios -> {1, 1, 1}];

p2 =
DensityPlot[f[x, y], {x, -5, 5}, {y, -5, 5},
ColorFunction -> "WatermelonColors",
PlotPoints -> 200,
PerformanceGoal -> "Quality",
Frame -> False,
PlotRangePadding -> None];

p3 = Plot3D[0, {x, -5, 5}, {y, -5, 5}, PlotStyle -> Texture[p2], Mesh -> None];

Show[p1, p3, PlotRange -> {0, 15}]


enter image description here



When I use StepMonitor to track iterations as follows, it does not work.



{fit, intermediates} = 
Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
MaxIterations -> 1000,
Method -> {"DifferentialEvolution", "InitialPoints" -> Tuples[Range[-5, 5], 2]},
StepMonitor :> Sow[{x, y}]]];

Table[
ListPlot[Take[intermediates[[1, i ;; i + 10]]],
Frame -> True, ImageSize -> 350, AspectRatio -> 1],
{i, 10, 1000, 100}]


EDIT
Here is the result when we used @Michael E2 solution. Cool!!



f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) - 
E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20

p1 = Plot3D[f[x, y], {x, -5, 5}, {y, -5, 5},
PerformanceGoal -> "Quality", ColorFunction -> "WatermelonColors",
Mesh -> None, BoxRatios -> {1, 1, 1}];

p2 = DensityPlot[f[x, y], {x, -5, 5}, {y, -5, 5},
ColorFunction -> "WatermelonColors", PerformanceGoal -> "Quality",
Frame -> False, PlotRangePadding -> None];

p3 = Plot3D[-0.5, {x, -5, 5}, {y, -5, 5}, PlotStyle -> Texture[p2],
Mesh -> None];

p4 = Show[p1, p3, PlotRange -> {-0.5, 15}]

Block[{f},
f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) -
E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20;
{fit, intermediates} =
Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
MaxIterations -> 30,
Method -> {"DifferentialEvolution",
"InitialPoints" -> Tuples[Range[-5, 5], 2]},
StepMonitor :>
Sow[{Optimization`NMinimizeDump`vecs,
Optimization`NMinimizeDump`vals}]]];] // Quiet

Multicolumn[
Table[Show[p4,
ListPointPlot3D[{Append[#, 0] & /@ intermediates[[1, i, 1]]},
PlotRange -> {{-5, 5}, {-5, 5}, {-5, 5}}, Boxed -> False,
PlotStyle -> Directive[AbsolutePointSize[3], Black]]], {i, 1, 30,
2}], 5, Appearance -> "Horizontal"]


enter image description here










share|improve this question











$endgroup$












  • $begingroup$
    Note that blocking f (Block[{f}, ...]) isn't necessary. It was just to prevent f from being defined, which is a habit I have with single-lettter symbols on SE, esp. ones I use like f, x, etc. -- thanks for the accept!
    $endgroup$
    – Michael E2
    49 mins ago


















4












$begingroup$


A differential evolution algorithm is given here. I would like to get this kind of animation. I thought I could use NMinimize, given
DifferentialEvolution as an option, but it turns out that does not work as I espected.



Is it possible to extract intermediate step in DifferentialEvolution, or do I have to implement algorithm myself?



f[x_, y_] := 
-20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) - E^(0.5 (Cos[2 π x] + Cos[2 π y])) + E + 20

p1 =
Plot3D[f[x, y], {x, -5, 5}, {y, -5, 5},
PerformanceGoal -> "Quality",
ColorFunction -> "WatermelonColors",
Mesh -> None,
BoxRatios -> {1, 1, 1}];

p2 =
DensityPlot[f[x, y], {x, -5, 5}, {y, -5, 5},
ColorFunction -> "WatermelonColors",
PlotPoints -> 200,
PerformanceGoal -> "Quality",
Frame -> False,
PlotRangePadding -> None];

p3 = Plot3D[0, {x, -5, 5}, {y, -5, 5}, PlotStyle -> Texture[p2], Mesh -> None];

Show[p1, p3, PlotRange -> {0, 15}]


enter image description here



When I use StepMonitor to track iterations as follows, it does not work.



{fit, intermediates} = 
Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
MaxIterations -> 1000,
Method -> {"DifferentialEvolution", "InitialPoints" -> Tuples[Range[-5, 5], 2]},
StepMonitor :> Sow[{x, y}]]];

Table[
ListPlot[Take[intermediates[[1, i ;; i + 10]]],
Frame -> True, ImageSize -> 350, AspectRatio -> 1],
{i, 10, 1000, 100}]


EDIT
Here is the result when we used @Michael E2 solution. Cool!!



f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) - 
E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20

p1 = Plot3D[f[x, y], {x, -5, 5}, {y, -5, 5},
PerformanceGoal -> "Quality", ColorFunction -> "WatermelonColors",
Mesh -> None, BoxRatios -> {1, 1, 1}];

p2 = DensityPlot[f[x, y], {x, -5, 5}, {y, -5, 5},
ColorFunction -> "WatermelonColors", PerformanceGoal -> "Quality",
Frame -> False, PlotRangePadding -> None];

p3 = Plot3D[-0.5, {x, -5, 5}, {y, -5, 5}, PlotStyle -> Texture[p2],
Mesh -> None];

p4 = Show[p1, p3, PlotRange -> {-0.5, 15}]

Block[{f},
f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) -
E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20;
{fit, intermediates} =
Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
MaxIterations -> 30,
Method -> {"DifferentialEvolution",
"InitialPoints" -> Tuples[Range[-5, 5], 2]},
StepMonitor :>
Sow[{Optimization`NMinimizeDump`vecs,
Optimization`NMinimizeDump`vals}]]];] // Quiet

Multicolumn[
Table[Show[p4,
ListPointPlot3D[{Append[#, 0] & /@ intermediates[[1, i, 1]]},
PlotRange -> {{-5, 5}, {-5, 5}, {-5, 5}}, Boxed -> False,
PlotStyle -> Directive[AbsolutePointSize[3], Black]]], {i, 1, 30,
2}], 5, Appearance -> "Horizontal"]


enter image description here










share|improve this question











$endgroup$












  • $begingroup$
    Note that blocking f (Block[{f}, ...]) isn't necessary. It was just to prevent f from being defined, which is a habit I have with single-lettter symbols on SE, esp. ones I use like f, x, etc. -- thanks for the accept!
    $endgroup$
    – Michael E2
    49 mins ago
















4












4








4


2



$begingroup$


A differential evolution algorithm is given here. I would like to get this kind of animation. I thought I could use NMinimize, given
DifferentialEvolution as an option, but it turns out that does not work as I espected.



Is it possible to extract intermediate step in DifferentialEvolution, or do I have to implement algorithm myself?



f[x_, y_] := 
-20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) - E^(0.5 (Cos[2 π x] + Cos[2 π y])) + E + 20

p1 =
Plot3D[f[x, y], {x, -5, 5}, {y, -5, 5},
PerformanceGoal -> "Quality",
ColorFunction -> "WatermelonColors",
Mesh -> None,
BoxRatios -> {1, 1, 1}];

p2 =
DensityPlot[f[x, y], {x, -5, 5}, {y, -5, 5},
ColorFunction -> "WatermelonColors",
PlotPoints -> 200,
PerformanceGoal -> "Quality",
Frame -> False,
PlotRangePadding -> None];

p3 = Plot3D[0, {x, -5, 5}, {y, -5, 5}, PlotStyle -> Texture[p2], Mesh -> None];

Show[p1, p3, PlotRange -> {0, 15}]


enter image description here



When I use StepMonitor to track iterations as follows, it does not work.



{fit, intermediates} = 
Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
MaxIterations -> 1000,
Method -> {"DifferentialEvolution", "InitialPoints" -> Tuples[Range[-5, 5], 2]},
StepMonitor :> Sow[{x, y}]]];

Table[
ListPlot[Take[intermediates[[1, i ;; i + 10]]],
Frame -> True, ImageSize -> 350, AspectRatio -> 1],
{i, 10, 1000, 100}]


EDIT
Here is the result when we used @Michael E2 solution. Cool!!



f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) - 
E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20

p1 = Plot3D[f[x, y], {x, -5, 5}, {y, -5, 5},
PerformanceGoal -> "Quality", ColorFunction -> "WatermelonColors",
Mesh -> None, BoxRatios -> {1, 1, 1}];

p2 = DensityPlot[f[x, y], {x, -5, 5}, {y, -5, 5},
ColorFunction -> "WatermelonColors", PerformanceGoal -> "Quality",
Frame -> False, PlotRangePadding -> None];

p3 = Plot3D[-0.5, {x, -5, 5}, {y, -5, 5}, PlotStyle -> Texture[p2],
Mesh -> None];

p4 = Show[p1, p3, PlotRange -> {-0.5, 15}]

Block[{f},
f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) -
E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20;
{fit, intermediates} =
Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
MaxIterations -> 30,
Method -> {"DifferentialEvolution",
"InitialPoints" -> Tuples[Range[-5, 5], 2]},
StepMonitor :>
Sow[{Optimization`NMinimizeDump`vecs,
Optimization`NMinimizeDump`vals}]]];] // Quiet

Multicolumn[
Table[Show[p4,
ListPointPlot3D[{Append[#, 0] & /@ intermediates[[1, i, 1]]},
PlotRange -> {{-5, 5}, {-5, 5}, {-5, 5}}, Boxed -> False,
PlotStyle -> Directive[AbsolutePointSize[3], Black]]], {i, 1, 30,
2}], 5, Appearance -> "Horizontal"]


enter image description here










share|improve this question











$endgroup$




A differential evolution algorithm is given here. I would like to get this kind of animation. I thought I could use NMinimize, given
DifferentialEvolution as an option, but it turns out that does not work as I espected.



Is it possible to extract intermediate step in DifferentialEvolution, or do I have to implement algorithm myself?



f[x_, y_] := 
-20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) - E^(0.5 (Cos[2 π x] + Cos[2 π y])) + E + 20

p1 =
Plot3D[f[x, y], {x, -5, 5}, {y, -5, 5},
PerformanceGoal -> "Quality",
ColorFunction -> "WatermelonColors",
Mesh -> None,
BoxRatios -> {1, 1, 1}];

p2 =
DensityPlot[f[x, y], {x, -5, 5}, {y, -5, 5},
ColorFunction -> "WatermelonColors",
PlotPoints -> 200,
PerformanceGoal -> "Quality",
Frame -> False,
PlotRangePadding -> None];

p3 = Plot3D[0, {x, -5, 5}, {y, -5, 5}, PlotStyle -> Texture[p2], Mesh -> None];

Show[p1, p3, PlotRange -> {0, 15}]


enter image description here



When I use StepMonitor to track iterations as follows, it does not work.



{fit, intermediates} = 
Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
MaxIterations -> 1000,
Method -> {"DifferentialEvolution", "InitialPoints" -> Tuples[Range[-5, 5], 2]},
StepMonitor :> Sow[{x, y}]]];

Table[
ListPlot[Take[intermediates[[1, i ;; i + 10]]],
Frame -> True, ImageSize -> 350, AspectRatio -> 1],
{i, 10, 1000, 100}]


EDIT
Here is the result when we used @Michael E2 solution. Cool!!



f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) - 
E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20

p1 = Plot3D[f[x, y], {x, -5, 5}, {y, -5, 5},
PerformanceGoal -> "Quality", ColorFunction -> "WatermelonColors",
Mesh -> None, BoxRatios -> {1, 1, 1}];

p2 = DensityPlot[f[x, y], {x, -5, 5}, {y, -5, 5},
ColorFunction -> "WatermelonColors", PerformanceGoal -> "Quality",
Frame -> False, PlotRangePadding -> None];

p3 = Plot3D[-0.5, {x, -5, 5}, {y, -5, 5}, PlotStyle -> Texture[p2],
Mesh -> None];

p4 = Show[p1, p3, PlotRange -> {-0.5, 15}]

Block[{f},
f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) -
E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20;
{fit, intermediates} =
Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
MaxIterations -> 30,
Method -> {"DifferentialEvolution",
"InitialPoints" -> Tuples[Range[-5, 5], 2]},
StepMonitor :>
Sow[{Optimization`NMinimizeDump`vecs,
Optimization`NMinimizeDump`vals}]]];] // Quiet

Multicolumn[
Table[Show[p4,
ListPointPlot3D[{Append[#, 0] & /@ intermediates[[1, i, 1]]},
PlotRange -> {{-5, 5}, {-5, 5}, {-5, 5}}, Boxed -> False,
PlotStyle -> Directive[AbsolutePointSize[3], Black]]], {i, 1, 30,
2}], 5, Appearance -> "Horizontal"]


enter image description here







mathematical-optimization






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited 1 hour ago







Okkes Dulgerci

















asked 3 hours ago









Okkes DulgerciOkkes Dulgerci

5,2691917




5,2691917












  • $begingroup$
    Note that blocking f (Block[{f}, ...]) isn't necessary. It was just to prevent f from being defined, which is a habit I have with single-lettter symbols on SE, esp. ones I use like f, x, etc. -- thanks for the accept!
    $endgroup$
    – Michael E2
    49 mins ago




















  • $begingroup$
    Note that blocking f (Block[{f}, ...]) isn't necessary. It was just to prevent f from being defined, which is a habit I have with single-lettter symbols on SE, esp. ones I use like f, x, etc. -- thanks for the accept!
    $endgroup$
    – Michael E2
    49 mins ago


















$begingroup$
Note that blocking f (Block[{f}, ...]) isn't necessary. It was just to prevent f from being defined, which is a habit I have with single-lettter symbols on SE, esp. ones I use like f, x, etc. -- thanks for the accept!
$endgroup$
– Michael E2
49 mins ago






$begingroup$
Note that blocking f (Block[{f}, ...]) isn't necessary. It was just to prevent f from being defined, which is a habit I have with single-lettter symbols on SE, esp. ones I use like f, x, etc. -- thanks for the accept!
$endgroup$
– Michael E2
49 mins ago












1 Answer
1






active

oldest

votes


















3












$begingroup$

Here's a way:



Block[{f},
f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) -
E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20;
{fit, intermediates} =
Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
MaxIterations -> 30,
Method -> {"DifferentialEvolution",
"InitialPoints" -> Tuples[Range[-5, 5], 2]},
StepMonitor :>
Sow[{Optimization`NMinimizeDump`vecs,
Optimization`NMinimizeDump`vals}]]];
]

Manipulate[
Graphics[{
PointSize[Medium],
Point[intermediates[[1, n, 1]],
VertexColors ->
ColorData["Rainbow"] /@
Rescale[intermediates[[1, n, 2]],
MinMax[intermediates[[1, All, 2]]]]]
},
PlotRange -> 5, Frame -> True],
{n, 1, Length@intermediates[[1]], 1}
]


enter image description here



You can find out about things like Optimization`NMinimizeDump`vecs by inspecting the code for Optimization`NMinimizeDump`CoreDE.






share|improve this answer









$endgroup$













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    1 Answer
    1






    active

    oldest

    votes








    1 Answer
    1






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    3












    $begingroup$

    Here's a way:



    Block[{f},
    f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) -
    E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20;
    {fit, intermediates} =
    Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
    MaxIterations -> 30,
    Method -> {"DifferentialEvolution",
    "InitialPoints" -> Tuples[Range[-5, 5], 2]},
    StepMonitor :>
    Sow[{Optimization`NMinimizeDump`vecs,
    Optimization`NMinimizeDump`vals}]]];
    ]

    Manipulate[
    Graphics[{
    PointSize[Medium],
    Point[intermediates[[1, n, 1]],
    VertexColors ->
    ColorData["Rainbow"] /@
    Rescale[intermediates[[1, n, 2]],
    MinMax[intermediates[[1, All, 2]]]]]
    },
    PlotRange -> 5, Frame -> True],
    {n, 1, Length@intermediates[[1]], 1}
    ]


    enter image description here



    You can find out about things like Optimization`NMinimizeDump`vecs by inspecting the code for Optimization`NMinimizeDump`CoreDE.






    share|improve this answer









    $endgroup$


















      3












      $begingroup$

      Here's a way:



      Block[{f},
      f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) -
      E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20;
      {fit, intermediates} =
      Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
      MaxIterations -> 30,
      Method -> {"DifferentialEvolution",
      "InitialPoints" -> Tuples[Range[-5, 5], 2]},
      StepMonitor :>
      Sow[{Optimization`NMinimizeDump`vecs,
      Optimization`NMinimizeDump`vals}]]];
      ]

      Manipulate[
      Graphics[{
      PointSize[Medium],
      Point[intermediates[[1, n, 1]],
      VertexColors ->
      ColorData["Rainbow"] /@
      Rescale[intermediates[[1, n, 2]],
      MinMax[intermediates[[1, All, 2]]]]]
      },
      PlotRange -> 5, Frame -> True],
      {n, 1, Length@intermediates[[1]], 1}
      ]


      enter image description here



      You can find out about things like Optimization`NMinimizeDump`vecs by inspecting the code for Optimization`NMinimizeDump`CoreDE.






      share|improve this answer









      $endgroup$
















        3












        3








        3





        $begingroup$

        Here's a way:



        Block[{f},
        f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) -
        E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20;
        {fit, intermediates} =
        Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
        MaxIterations -> 30,
        Method -> {"DifferentialEvolution",
        "InitialPoints" -> Tuples[Range[-5, 5], 2]},
        StepMonitor :>
        Sow[{Optimization`NMinimizeDump`vecs,
        Optimization`NMinimizeDump`vals}]]];
        ]

        Manipulate[
        Graphics[{
        PointSize[Medium],
        Point[intermediates[[1, n, 1]],
        VertexColors ->
        ColorData["Rainbow"] /@
        Rescale[intermediates[[1, n, 2]],
        MinMax[intermediates[[1, All, 2]]]]]
        },
        PlotRange -> 5, Frame -> True],
        {n, 1, Length@intermediates[[1]], 1}
        ]


        enter image description here



        You can find out about things like Optimization`NMinimizeDump`vecs by inspecting the code for Optimization`NMinimizeDump`CoreDE.






        share|improve this answer









        $endgroup$



        Here's a way:



        Block[{f},
        f[x_, y_] := -20 E^(-0.2 Sqrt[0.5 (x^2 + y^2)]) -
        E^(0.5 (Cos[2 [Pi] x] + Cos[2 [Pi] y])) + E + 20;
        {fit, intermediates} =
        Reap[NMinimize[{f[x, y], -5 <= x <= 5, -5 <= y <= 5}, {x, y},
        MaxIterations -> 30,
        Method -> {"DifferentialEvolution",
        "InitialPoints" -> Tuples[Range[-5, 5], 2]},
        StepMonitor :>
        Sow[{Optimization`NMinimizeDump`vecs,
        Optimization`NMinimizeDump`vals}]]];
        ]

        Manipulate[
        Graphics[{
        PointSize[Medium],
        Point[intermediates[[1, n, 1]],
        VertexColors ->
        ColorData["Rainbow"] /@
        Rescale[intermediates[[1, n, 2]],
        MinMax[intermediates[[1, All, 2]]]]]
        },
        PlotRange -> 5, Frame -> True],
        {n, 1, Length@intermediates[[1]], 1}
        ]


        enter image description here



        You can find out about things like Optimization`NMinimizeDump`vecs by inspecting the code for Optimization`NMinimizeDump`CoreDE.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered 2 hours ago









        Michael E2Michael E2

        148k12198478




        148k12198478






























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