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Fix image references and name changes.

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Fabian Becker 2014-11-12 19:23:03 +01:00
parent 014fd85beb
commit 9ea3f4d464
10 changed files with 21 additions and 22 deletions
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resources/html/Default.html
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<title>Default page</title> <title>Default page</title>
</head> </head>
<body> <body>
<EFBFBD>
<h1 align="center">HTML description file is missing</h1> <h1 align="center">HTML description file is missing</h1>
<center> <center>
</center><br> </center><br>

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resources/html/F13Problem.html
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<body> <body>
<h1 align="center">Schwefel's (sine root) function</h1> <h1 align="center">Schwefel's (sine root) function</h1>
<center> <center>
<img src="images/f13-tex-500.jpg" width="650" height="64" aling="center"> <img src="../images/f13-tex-500.jpg" width="650" height="64" aling="center">
</center> </center>
<p> <p>
Schwefel's (sine root) function is highly multimodal and has no global basin of attraction. The optimum at a fitness of f(x*)=0 lies at x*=420.9687. Schwefel's sine root is a tough challenge for any global optimizer due to the multiple distinct optima. Especially, there is a deceptive nearly optimal solution close to x=(-420.9687)<SUP>n</SUP>. Schwefel's (sine root) function is highly multimodal and has no global basin of attraction. The optimum at a fitness of f(x*)=0 lies at x*=420.9687. Schwefel's sine root is a tough challenge for any global optimizer due to the multiple distinct optima. Especially, there is a deceptive nearly optimal solution close to x=(-420.9687)<SUP>n</SUP>.
@ -14,7 +14,7 @@ Schwefel's (sine root) function is highly multimodal and has no global basin of
<p> <p>
<img src="images/f13-schwefels-sine-root.jpg" width="667" height="493" border="2" align="center"> <img src="../images/f13-schwefels-sine-root.jpg" width="667" height="493" border="2" align="center">
<br> <br>
Schwefels's sine root function in 2D within the co-domain -500 <= <i>x</i> <= 500. Schwefels's sine root function in 2D within the co-domain -500 <= <i>x</i> <= 500.
<p> <p>

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resources/html/F1Problem.html
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<body> <body>
<h1 align="center">The F1 hyper-parabola function</h1> <h1 align="center">The F1 hyper-parabola function</h1>
<center> <center>
<img src="images/f1tex.jpg" width="85" height="95" border="0" align="center"> <img src="../images/f1tex.jpg" width="85" height="95" border="0" align="center">
</center><br> </center><br>
The hyper-parabola function is a <i>n</i>-dimensional, axis-symmetric, continuously differentiable, convex function: The hyper-parabola function is a <i>n</i>-dimensional, axis-symmetric, continuously differentiable, convex function:
<p> <p>
Because of its simplicity every optimization-algorithm should be able to find its global minimum at <i>x</i>=[0, 0, ... , 0] Because of its simplicity every optimization-algorithm should be able to find its global minimum at <i>x</i>=[0, 0, ... , 0]
<p> <p>
<img src="images/f1.jpg" width="480" height="360" border="2" align="middle"> <img src="../images/f1.jpg" width="480" height="360" border="2" align="middle">
<hr> <hr>
More information about the F1 function can be found at: More information about the F1 function can be found at:

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resources/html/F2Problem.html
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<body> <body>
<h1 align="center">Generalized Rosenbrock's function</h1> <h1 align="center">Generalized Rosenbrock's function</h1>
<center> <center>
<img src="images/rosenbrocktex.jpg" width="500" height="78"> <img src="../images/rosenbrocktex.jpg" width="500" height="78">
</center> </center>
<p> <p>
This function is unimodal and continuous, but the global optimum is hard to find, because of independence through the term (<i>x</i>_(<i>i</i>+1) - <i>x_i</i>*<i>x_i</i>)^2 between contiguous parameters. This function is unimodal and continuous, but the global optimum is hard to find, because of independence through the term (<i>x</i>_(<i>i</i>+1) - <i>x_i</i>*<i>x_i</i>)^2 between contiguous parameters.
<p> <p>
<img src="images/f85.jpg" border="2"> <img src="../images/f85.jpg" border="2">
<br> <br>
Rosenbrock's function within the domain -5 <= <i>x</i> <= 5. Rosenbrock's function within the domain -5 <= <i>x</i> <= 5.
<p> <p>
The global optimum is located in a parabolic formed valley (among the curve x^2 = x_1^2), which has a flat ground. The global optimum is located in a parabolic formed valley (among the curve x^2 = x_1^2), which has a flat ground.
<br> <br>
<img src="images/f81.jpg" border="2"> <img src="../images/f81.jpg" border="2">
<br> <br>
The function close to its global optimum, which is: f(<i>x</i>) = f(1, 1, ... , 1) = 0. The function close to its global optimum, which is: f(<i>x</i>) = f(1, 1, ... , 1) = 0.
<p> <p>

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resources/html/F3Problem.html
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<body> <body>
<h1 align="center">The Step Function</h1> <h1 align="center">The Step Function</h1>
<center> <center>
<img src="images/steptex.jpg" width="350" height="120" aling="center"> <img src="../images/steptex.jpg" width="350" height="120" aling="center">
</center> </center>
<p> <p>
The idea of this function is the implementation of a flat plateau (slope 0)in an underlying continuous function. It's harder for optimization algortihms to find optima because minor changes of the object variables don't affect the fitness. Therefore no conclusions about the search direction can be made. The idea of this function is the implementation of a flat plateau (slope 0)in an underlying continuous function. It's harder for optimization algortihms to find optima because minor changes of the object variables don't affect the fitness. Therefore no conclusions about the search direction can be made.
<p> <p>
<img src="images/step5.jpg" width="480" height="360" border="2" align="center"> <img src="../images/step5.jpg" width="480" height="360" border="2" align="center">
<p> <p>
The step function is symmetric considering the underlying function (here: f(x,y) = f(y,x)), but between the bulk constant plateau-areas not continuously differentiable. The step function is symmetric considering the underlying function (here: f(x,y) = f(y,x)), but between the bulk constant plateau-areas not continuously differentiable.
<p> <p>
Its minimum-area is located in the intervals: <i>f(x)</i>=<i>f</i>([-5.12,-5), ... , [-5.12,-5))=0. Its minimum-area is located in the intervals: <i>f(x)</i>=<i>f</i>([-5.12,-5), ... , [-5.12,-5))=0.
<p> <p>
<img src="images/stepopt.jpg" width="480" height="360" border="2" align="center"> <img src="../images/stepopt.jpg" width="480" height="360" border="2" align="center">
<hr> <hr>
More information about the step function can be found at: More information about the step function can be found at:
<p> <p>

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<body> <body>
<h1 align="center">Schwefels double sum</h1> <h1 align="center">Schwefels double sum</h1>
<center> <center>
<img src="images/f2tex.jpg" width="220" height="102" border="0" align="center"> <img src="../images/f2tex.jpg" width="220" height="102" border="0" align="center">
</center> </center>
<p> <p>
Schwefel's double sum is a quadratic minimization problem. Its difficulty increases by the dimension <i>n</i> in <i>O(n^2)</i>. It is used for analysis of correlating mutations. Schwefel's double sum is a quadratic minimization problem. Its difficulty increases by the dimension <i>n</i> in <i>O(n^2)</i>. It is used for analysis of correlating mutations.
<p> <p>
It possesses specific symmetrical properties:<br> It possesses specific symmetrical properties:<br>
<img src="images/schwefelsymmetrie.jpg" width="500" height="104" border="0" align="middle"> <img src="../images/schwefelsymmetrie.jpg" width="500" height="104" border="0" align="middle">
<p> <p>
Its minimum is located at: <i>f(x)</i>=<i>f</i>([0, 0, ... , 0])=0 Its minimum is located at: <i>f(x)</i>=<i>f</i>([0, 0, ... , 0])=0
<p> <p>
<img src="images/f2.jpg" width="480" height="360" border="2" align="middle"> <img src="../images/f2.jpg" width="480" height="360" border="2" align="middle">
<hr> <hr>
More information about Schwefel's double sum can be found at: More information about Schwefel's double sum can be found at:

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<body> <body>
<h1 align="center">Generalized Rastrigin's function</h1> <h1 align="center">Generalized Rastrigin's function</h1>
<center> <center>
<img src="images/rastrigintex.jpg" width="500" height="101"> <img src="../images/rastrigintex.jpg" width="500" height="101">
</center> </center>
<p> <p>
Rastrigin's function is symmetric. It is based on the simple <i>parabola function</i> (called f1 in the EvA context), but it is multimodal because a modulation term on the basis of the cosine function is added. This evokes hills and valleys which are misleading local optima. Rastrigin's function is symmetric. It is based on the simple <i>parabola function</i> (called f1 in the EvA context), but it is multimodal because a modulation term on the basis of the cosine function is added. This evokes hills and valleys which are misleading local optima.
@ -13,14 +13,14 @@ Rastrigin's function is symmetric. It is based on the simple <i>parabola functio
Values used for the following illustrations: <i>A</i>=10, <i>&#969;</i>=2*&#960;, <i>n</i>=2. Values used for the following illustrations: <i>A</i>=10, <i>&#969;</i>=2*&#960;, <i>n</i>=2.
<br> <br>
<img src="images/rastrigin20.jpg" border="2"> <img src="../images/rastrigin20.jpg" border="2">
<br> <br>
Rastrigin's function within the co-domain -20>=<i>x</i>>=20 Rastrigin's function within the co-domain -20>=<i>x</i>>=20
<p> <p>
<img src="images/rastrigin5.jpg" border="2"> <img src="../images/rastrigin5.jpg" border="2">
<br> <br>
Rastrigin's function within the co-domain -5>=<i>x</i>>=5 Rastrigin's function within the co-domain -5>=<i>x</i>>=5
@ -29,7 +29,7 @@ Rastrigin's function within the co-domain -5>=<i>x</i>>=5
Like Ackley's function a simple evolutionary algorithm would get stuck in a local optimum, while a broader searching algorithm would get out of the local optimum closer to the global optimum, which in this case is: f(<i>x</i>) = f(0, 0, ... , 0) = 0. Like Ackley's function a simple evolutionary algorithm would get stuck in a local optimum, while a broader searching algorithm would get out of the local optimum closer to the global optimum, which in this case is: f(<i>x</i>) = f(0, 0, ... , 0) = 0.
<p> <p>
<img src="images/rastrigin1.jpg" border="2"><br> <img src="../images/rastrigin1.jpg" border="2"><br>
Rastrigin's function close to its optimum. Rastrigin's function close to its optimum.
<hr> <hr>

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resources/html/F8Problem.html
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<body> <body>
<h1 align="center">Ackley's function</h1> <h1 align="center">Ackley's function</h1>
<center> <center>
<img src="images/ackleytex.jpg" width="500" height="58" aling="center"> <img src="../images/ackleytex.jpg" width="500" height="58" aling="center">
</center> </center>
<p> <p>
Ackley's function is multimodal and symmetrical. It is based on an exponential function and modulated by a cosine function. Ackley's function is multimodal and symmetrical. It is based on an exponential function and modulated by a cosine function.
@ -17,12 +17,12 @@ The difficulty for an optimization algorithm is mid-graded because a simple opti
<p> <p>
<img src="images/ackley.jpg" width="480" height="360" border="2" align="center"> <img src="../images/ackley.jpg" width="480" height="360" border="2" align="center">
<br> <br>
Ackley's function within the co-domain -20 >= <i>x</i> >= 20, <i>a</i>=20, <i>b</i>=0.2, <i>c</i>=2*&#960;, <i>n</i>=2. Ackley's function within the co-domain -20 >= <i>x</i> >= 20, <i>a</i>=20, <i>b</i>=0.2, <i>c</i>=2*&#960;, <i>n</i>=2.
<p> <p>
<img src="images/ackleyopt.jpg" width="480" height="360" border="2" align="center"> <img src="../images/ackleyopt.jpg" width="480" height="360" border="2" align="center">
<br> <br>
Ackley's function close to the optimum. Ackley's function close to the optimum.
<hr> <hr>

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resources/html/StatisticsParameterImpl.html → resources/html/StatisticsParameters.html
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src/eva2/gui/Exp.java → src/eva2/gui/plot/Exp.java
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package eva2.gui; package eva2.gui.plot;
import eva2.tools.chart2d.DFunction; import eva2.tools.chart2d.DFunction;