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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.  

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.  

The EA can often discover optimal solutions that human being's have never thought of. The Evolvable Systems Group at NASA recently used a genetic algorithm to find an antenna design far superior to any previously created.<ref>http://ic.arc.nasa.gov/projects/esg/research/antenna.htm</ref> This ability of the EA to produce solutions that human beings have never even thought of is often used to attest to the power of evolutionary mechanism to produce novel and efficient solutions to problem sets. <ref>http://www.stewdean.com/alife/evolution.html</ref>

However, in the last decade, research on evolutionary algorithms has fallen off sharply, 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 Algorithms such as [[simulated annealing]], and fast [[integer programming]] solvers have largely superceded evolutionary algorithms in modern use.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 rejection of that theory.  

==See Also==

==See Also==