Documentation/Modules/ConstraintHandler - Versionsgeschichte

Dirk: /* Module Description */

2015-10-26T21:00:06Z

‎Module Description

Dirk am 25. Oktober 2015 um 18:54 Uhr

2015-10-25T18:54:05Z

Dirk: /* Summary */

2015-10-25T18:53:19Z

‎Summary

Dirk: /* Module Description */

2015-10-25T18:52:07Z

‎Module Description

Dirk: /* References */

2015-10-25T18:18:36Z

‎References

Dirk: /* Source Code */

2015-10-25T18:18:23Z

‎Source Code

Dirk: /* Usage */

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‎Usage

Dirk: /* Usage */

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‎Usage

Dirk: /* References */

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‎References

Dirk: /* References */

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‎References

@@ Zeile 72: / Zeile 72: @@
 While bjectives are to be minimized (<code>min('''f''')</code>), constraints are to be fulfilled (<code>'''g''' =< 0</code>).
-'''0. no constraint handling'''
+'''0. No Constraint Handling'''
 This option turns off the constraint handling. Objective values and constraints are not changed.
-'''1. delete constraints'''
+'''1. Delete Constraints'''
 Deletes all constraints. This can be used if the goal is to minimize the objective function(s) only. The objective functions are not changed.
-'''2. Penalty method'''
+'''2. Penalty Method'''
-All objective function values are summed. All violated constraints (i.e. the constraint value is >= 0) are summed and multiplied with a penalty factor <code>p</code>:
+To each objective function, a penalty (larger or equal to zero) is added. All violated constraints (i.e. the constraint value is >= 0) are summed and multiplied with a penalty factor <code>p</code>:
-   output = sum<sub>i</sub>(f<sub>i</sub>) + sum<sub>j</sub>(max(g<sub>j</sub>,0))*p
+   penalized objective i = f<sub>i</sub> + sum<sub>j</sub>(max(g<sub>j</sub>,0))*p
 Increasing the penalty factor <code>p</code> puts more weight on the constraints compared to the objectives.
-'''3. Penalty method'''
+'''3. Stochastic Ranking'''
-This algorithm works only for population based search methods such as CMA-ES. There are 3 steps
+Limitations:
-  a. All objective function values are summed: sum<sub>i</sub>(f<sub>i</sub>)
+Rough Description of Main Steps of Stochastic Ranking ([[#1|1]])
-  b. All violated constraints (i.e. the constraint value is >= 0) are summed: sum<sub>j</sub>(max(g<sub>j</sub>,0))
+# For each solution, the penalty is computed as the sum of all violated constraints (i.e. the constraint value is >= 0) <code> p = sum<sub>j</sub>(max(g<sub>j</sub>,0)) </code> as in the Penalty Method.
-  c. Ranking of the solutions according to the Stochastic Ranking ([[#1|1]])
+# The solutions of the population are randomly ranked.
 ==Usage==

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−	[[Documentation/Modules \| << UP TO MODULE OVERVIEW <<]]	+	[[Documentation/Modules \| << BACK TO MODULE OVERVIEW <<]]

	==Summary==		==Summary==

← Nächstältere Version		Version vom 25. Oktober 2015, 18:53 Uhr
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		+	[[Documentation/Modules \| << UP TO MODULE OVERVIEW <<]]
		+
	==Summary==		==Summary==

@@ Zeile 70: / Zeile 70: @@
 While bjectives are to be minimized (<code>min('''f''')</code>), constraints are to be fulfilled (<code>'''g''' =< 0</code>).
-. no constraint handling
+'''0. no constraint handling'''
 This option turns off the constraint handling. Objective values and constraints are not changed.
-. delete constraints
+'''1. delete constraints'''
 Deletes all constraints. This can be used if the goal is to minimize the objective function(s) only. The objective functions are not changed.
-. Penalty method
+'''2. Penalty method'''
 All objective function values are summed. All violated constraints (i.e. the constraint value is >= 0) are summed and multiplied with a penalty factor <code>p</code>:
@@ Zeile 86: / Zeile 86: @@
 Increasing the penalty factor <code>p</code> puts more weight on the constraints compared to the objectives.
-. Penalty method
+'''3. Penalty method'''
 This algorithm works only for population based search methods such as CMA-ES. There are 3 steps

← Nächstältere Version		Version vom 25. Oktober 2015, 18:18 Uhr
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	==References==		==References==
−	(<span id="1">1</span>) T. P. Runarsson and X. Yao, "Stochastic Ranking for Constrained Evolutionary Optimization", '''IEEE Transactions on Evolutionary Computation''', Vol. 4, No. 3, pp. 284-294, Sep. 2000.	+	(<span id="1">1</span>) T. P. Runarsson and X. Yao, "Stochastic Ranking for Constrained Evolutionary Optimization", ''IEEE Transactions on Evolutionary Computation'', Vol. 4, No. 3, pp. 284-294, Sep. 2000.

@@ Zeile 100: / Zeile 100: @@
 ==Source Code==
-ToDo:Link to SVN
+https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/optim/tools/ConstraintHandler.java
 ==References==
 (<span id="1">1</span>) T. P. Runarsson and X. Yao, "Stochastic Ranking for Constrained Evolutionary Optimization", '''IEEE Transactions on Evolutionary Computation''', Vol. 4, No. 3, pp. 284-294, Sep. 2000.

← Nächstältere Version		Version vom 25. Oktober 2015, 18:18 Uhr
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	==Usage==		==Usage==
−	-	+
		+	https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/optim/tools/ConstraintHandler.java
		+
	==Source Code==		==Source Code==

← Nächstältere Version		Version vom 25. Oktober 2015, 18:16 Uhr
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	==References==		==References==
−	(<~~div~~ id="1">1</~~div~~>) T. P. Runarsson and X. Yao, "Stochastic Ranking for Constrained Evolutionary Optimization", '''IEEE Transactions on Evolutionary Computation''', Vol. 4, No. 3, pp. 284-294, Sep. 2000.	+	(<span id="1">1</span>) T. P. Runarsson and X. Yao, "Stochastic Ranking for Constrained Evolutionary Optimization", '''IEEE Transactions on Evolutionary Computation''', Vol. 4, No. 3, pp. 284-294, Sep. 2000.