Evolutionary algorithm - Revision history

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Popularity and usage

← Older revision		Revision as of 01:12, July 25, 2020
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−	An '''Evolutionary algorithm''' (EA) is a stochastic numerical analysis that takes its inspiration from the [[theory of evolution]], particularly the optimization power of [[natural selection]]. The main thing that sets an evolutionary algorithm apart from other stochastic methods is the use of a [[fitness function]] to select for optimal solutions. New solutions are a created by allowing existing ones to breed with each other. Also many [[algorithms~~\|Algorithm~~]] use random alterations in the coded solution similar to the biological principle of [[mutation]]. The fitness program selects solutions that better solve the problem and increases the frequency of that solution and its descendants in the over all population of solutions.	+	An '''Evolutionary algorithm''' (EA) is a stochastic numerical analysis that takes its inspiration from the [[theory of evolution]], particularly the optimization power of [[natural selection]]. The main thing that sets an evolutionary algorithm apart from other stochastic methods is the use of a [[fitness function]] to select for optimal solutions. New solutions are a created by allowing existing ones to breed with each other. Also many [[Algorithm\|algorithms]] use random alterations in the coded solution similar to the biological principle of [[mutation]]. The fitness program selects solutions that better solve the problem and increases the frequency of that solution and its descendants in the over all population of solutions.

	==Types of Evolutionary algorithms==		==Types of Evolutionary algorithms==

← Older revision		Revision as of 01:12, July 25, 2020
Line 1:		Line 1:
−	An '''Evolutionary algorithm''' (EA) is a stochastic numerical analysis that takes its inspiration from the [[theory of evolution]], particularly the optimization power of [[natural selection]]. The main thing that sets an evolutionary algorithm apart from other stochastic methods is the use of a [[fitness function]] to select for optimal solutions. New solutions are a created by allowing existing ones to breed with each other. Also many algorithms use random alterations in the coded solution similar to the biological principle of [[mutation]]. The fitness program selects solutions that better solve the problem and increases the frequency of that solution and its descendants in the over all population of solutions.	+	An '''Evolutionary algorithm''' (EA) is a stochastic numerical analysis that takes its inspiration from the [[theory of evolution]], particularly the optimization power of [[natural selection]]. The main thing that sets an evolutionary algorithm apart from other stochastic methods is the use of a [[fitness function]] to select for optimal solutions. New solutions are a created by allowing existing ones to breed with each other. Also many [[algorithms\|Algorithm]] use random alterations in the coded solution similar to the biological principle of [[mutation]]. The fitness program selects solutions that better solve the problem and increases the frequency of that solution and its descendants in the over all population of solutions.

	==Types of Evolutionary algorithms==		==Types of Evolutionary algorithms==

@@ Line 12: / Line 12: @@
 Evolutionary algorithms are general purpose optimizers because they do not require any assumptions about the landscape of the fitness function. They are used in a wide range of problems in diverse fields and have proven to be a highly effective numerical analysis method.
-However, in the last decade, research on evolutionary algorithms has fallen off sharply{{Citation needed|date=May 2010}}, and they have not lived up to their initial promise. Although they are a reasonable search technique in a wide variety of problems, they are not the best search technique in almost any field. Algorithms such as [[simulated annealing]], and fast [[integer programming]] solvers have largely superceded evolutionary algorithms in modern use. Evolutionary algorithms can be seen as an experimental test of Darwin's theory of evolution, and their eventual failure can be seen as a refutation of that theory{{Citation needed|date=May 2010}}.
+However, in the last decade, research on evolutionary algorithms has fallen off sharply{{Citation needed|date=May 2010}}, and they have not lived up to their initial promise. Although they are a reasonable search technique in a wide variety of problems, they are not the best search technique in almost any field. Algorithms such as [[simulated annealing]], and fast [[integer programming]] solvers have largely superseded evolutionary algorithms in modern use. Evolutionary algorithms can be seen as an experimental test of Darwin's theory of evolution, and their eventual failure can be seen as a refutation of that theory{{Citation needed|date=May 2010}}.
 ==See also==

@@ Line 12: / Line 12: @@
 Evolutionary algorithms are general purpose optimizers because they do not require any assumptions about the landscape of the fitness function. They are used in a wide range of problems in diverse fields and have proven to be a highly effective numerical analysis method.
-However, in the last decade, research on evolutionary algorithms has fallen off sharply{{Citation needed|date=May 2010}}, and they have not lived up to their initial promise. Although they are a reasonable search technique in a wide variety of problems, they are not the best search technique in almost any field. Algorithms such as [[simulated annealing]], and fast [[integer programming]] solvers have largely superseded evolutionary algorithms in modern use. Evolutionary algorithms can be seen as an experimental test of Darwin's theory of evolution, and their eventual failure can be seen as a refutation of that theory{{Citation needed|date=May 2010}}.
+However, in the last decade, research on evolutionary algorithms has fallen off sharply{{Citation needed|date=May 2010}}, and they have not lived up to their initial promise. Although they are a reasonable search technique in a wide variety of problems, they are not the best search technique in almost any field. Algorithms such as [[simulated annealing]], and fast [[integer programming]] solvers have largely superceded evolutionary algorithms in modern use. Evolutionary algorithms can be seen as an experimental test of Darwin's theory of evolution, and their eventual failure can be seen as a refutation of that theory{{Citation needed|date=May 2010}}.
 ==See also==

@@ Line 12: / Line 12: @@
 Evolutionary algorithms are general purpose optimizers because they do not require any assumptions about the landscape of the fitness function. They are used in a wide range of problems in diverse fields and have proven to be a highly effective numerical analysis method.
-However, in the last decade, research on evolutionary algorithms has fallen off sharply{{Citation needed|date=May 2010}}, and they have not lived up to their initial promise. Although they are a reasonable search technique in a wide variety of problems, they are not the best search technique in almost any field. Algorithms such as [[simulated annealing]], and fast [[integer programming]] solvers have largely superceded evolutionary algorithms in modern use. Evolutionary algorithms can be seen as an experimental test of Darwin's theory of evolution, and their eventual failure can be seen as a refutation of that theory{{Citation needed|date=May 2010}}.
+However, in the last decade, research on evolutionary algorithms has fallen off sharply{{Citation needed|date=May 2010}}, and they have not lived up to their initial promise. Although they are a reasonable search technique in a wide variety of problems, they are not the best search technique in almost any field. Algorithms such as [[simulated annealing]], and fast [[integer programming]] solvers have largely superseded evolutionary algorithms in modern use. Evolutionary algorithms can be seen as an experimental test of Darwin's theory of evolution, and their eventual failure can be seen as a refutation of that theory{{Citation needed|date=May 2010}}.
 ==See also==

@@ Line 14: / Line 14: @@
 However, in the last decade, research on evolutionary algorithms has fallen off sharply{{Citation needed|date=May 2010}}, and they have not lived up to their initial promise. Although they are a reasonable search technique in a wide variety of problems, they are not the best search technique in almost any field. Algorithms such as [[simulated annealing]], and fast [[integer programming]] solvers have largely superceded evolutionary algorithms in modern use. Evolutionary algorithms can be seen as an experimental test of Darwin's theory of evolution, and their eventual failure can be seen as a refutation of that theory{{Citation needed|date=May 2010}}.
-==See Also==
+==See also==
 [[Monte Carlo method]]
@@ Line 23: / Line 23: @@
 ==References==
 <references />
-[[category:mathematics]]
+[[Category:Mathematics]]
 [[Category:Computer Science]]

← Older revision		Revision as of 05:34, December 4, 2009
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−	An '''Evolutionary algorithm''' (EA) is a stochastic numerical analysis that takes its inspiration from the [[theory of evolution]], particularly the optimization power of [[natural selection]]. The main thing that sets an evolutionary algorithm apart from other stochastic methods is the use of a [[fitness function]] to select for optimal solutions. New solutions are a created by allowing existing ones to breed with each other. Also many algorithms use random alterations in the coded solution similar to the biological principle of [[mutation]]. The fitness program selects solutions that better solve the problem and increases the frequency of that solution and its descendants in the over all population of solutions~~. John Schlafly is gay~~.	+	An '''Evolutionary algorithm''' (EA) is a stochastic numerical analysis that takes its inspiration from the [[theory of evolution]], particularly the optimization power of [[natural selection]]. The main thing that sets an evolutionary algorithm apart from other stochastic methods is the use of a [[fitness function]] to select for optimal solutions. New solutions are a created by allowing existing ones to breed with each other. Also many algorithms use random alterations in the coded solution similar to the biological principle of [[mutation]]. The fitness program selects solutions that better solve the problem and increases the frequency of that solution and its descendants in the over all population of solutions.

	==Types of Evolutionary algorithms==		==Types of Evolutionary algorithms==

← Older revision		Revision as of 05:31, December 4, 2009
Line 1:		Line 1:
−	An '''Evolutionary algorithm''' (EA) is a stochastic numerical analysis that takes its inspiration from the [[theory of evolution]], particularly the optimization power of [[natural selection]]. The main thing that sets an evolutionary algorithm apart from other stochastic methods is the use of a [[fitness function]] to select for optimal solutions. New solutions are a created by allowing existing ones to breed with each other. Also many algorithms use random alterations in the coded solution similar to the biological principle of [[mutation]]. The fitness program selects solutions that better solve the problem and increases the frequency of that solution and its descendants in the over all population of solutions.	+	An '''Evolutionary algorithm''' (EA) is a stochastic numerical analysis that takes its inspiration from the [[theory of evolution]], particularly the optimization power of [[natural selection]]. The main thing that sets an evolutionary algorithm apart from other stochastic methods is the use of a [[fitness function]] to select for optimal solutions. New solutions are a created by allowing existing ones to breed with each other. Also many algorithms use random alterations in the coded solution similar to the biological principle of [[mutation]]. The fitness program selects solutions that better solve the problem and increases the frequency of that solution and its descendants in the over all population of solutions. John Schlafly is gay.

	==Types of Evolutionary algorithms==		==Types of Evolutionary algorithms==