02c84001d4
Update dead gforge.inria.fr and eodev.sourceforge.net URLs in the module-level Doxygen index pages (eo, mo, moeo, smp, DTLZ). Replace with GitHub URLs and current contact information.
62 lines
3.5 KiB
C
62 lines
3.5 KiB
C
/** @mainpage Welcome to Evolving Objects
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@section shortcuts In one word
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The best place to learn about the features and approaches of %EO with the help of examples is to look at
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the <a href="../../tutorial/html/eoTutorial.html">tutorial</a>.
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Once you have understand the @ref design of %EO, you could search for advanced features by browsing the <a
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href="modules.html">modules</a> page.
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@section intro Introduction
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%EO is a template-based, ANSI-C++ evolutionary computation library which helps you to write your own stochastic optimization algorithms insanely fast.
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It contains classes for almost any kind of evolutionary computation you might come
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up to - at least for the ones we could think of. It is component-based, so that
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if you don't find the class you need in it, it is very easy to subclass existing
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abstract or concrete classes.
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Designing an algorithm with %EO consists in choosing what components you want to use for your specific needs, just as building a structure with Lego blocks.
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If you have a classical problem for which available code exists (for example if you have a black-box problem with real-valued variables), you will just choose components to form an algorithm and connect it to your fitness function (which computes the quality of a given solution).
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If your problem is a bit more exotic, you will have to code a class that represents how your individuals (a solution to your problem) are represented, and perhaps some variations operators, but most of the other operators (selection, replacement, stopping criteria, command-line interface, etc.) are already available in %EO.
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@section design Overall Design
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%EO is a framework. It is oriented toward facilitating the design of adhoc evolutionary algorithms.
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It is not (at the moment) a complete library of algorithms ready to use on canonical problems.
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If you have a well-known problem and want to solve it as soon as possible, try another software.
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If you have a real problem and want to build the best evolutionary algorithm to solve it, you've made
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the good choice.
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Bascially, %EO manipulate "individuals" with a "fitness", that is objects
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encoding a solution to a given optimization problem, associated with
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the quality of this solution. The fitness is defined in the %EO class,
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but the representation of a solution cannot be as generic. Thus, %EO
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massively use templates, so that you will not be limited by interfaces
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when using your own representation.
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Once you have a representation, you will build your own evolutionary algorithm
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by assembling @ref Operators in @ref Algorithms.
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In %EO, most of the objects are functors, that is classes with an operator(), that you
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can call just as if they were classical functions. For example, an algorithm is a
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functor, that manipulate a population of individuals, it will be implemented as a functor,
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with a member like: operator()(eoPop<EOT>). Once called on a given population, it will
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search for the optimum of a given problem.
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Generally, operators are instanciated once and then binded in an algorithm by reference.
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Thus, you can easily build your own algorithm by trying several combination of operators.
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For a more detailled introduction to the design of %EO you can look at the
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slides from a talk at EA 2001 or at the corresponding
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article in Lecture Notes In Computer Science, 2310, Selected Papers from the 5th European Conference on Artificial Evolution:
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- https://doi.org/10.1007/3-540-46033-0_13
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- LeCreusot.pdf (available in eo/doc/)
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- EO_EA2001.pdf (available in eo/doc/)
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*/
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