Algorithms

In EO, an algorithm is a functor that takes one or several solutions to an optimization problem as arguments, and iteratively modify them with the help of operators. More...

Classes
class	eoAlgo< EOT >
	This is the base class for population-transforming algorithms. More...
class	eoCellularEasyEA< EOT >
	The abstract cellular easy algorithm. More...
class	eoEasyEA< EOT >
	An easy-to-use evolutionary algorithm; you can use any chromosome, and any selection transformation, merging and evaluation algorithms; you can even change in runtime parameters of those sub-algorithms. More...
class	eoEasyPSO< POT >
	An easy-to-use particle swarm algorithm. More...
class	eoEDA< EOT >
	The abstract class for estimation of disribution algorithms. More...
class	eoPSO< POT >
	This is a generic class for particle swarm algorithms. More...
class	eoSGA< EOT >
	The Simple Genetic Algorithm, following Holland and Goldberg. More...
class	eoSimpleEDA< EOT >
	A very simple Estimation of Distribution Algorithm. More...
class	eoSyncEasyPSO< POT >
	An easy-to-use synchronous particle swarm algorithm; you can use any particle, any flight, any topology... More...

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Detailed Description

In EO, an algorithm is a functor that takes one or several solutions to an optimization problem as arguments, and iteratively modify them with the help of operators.

Generally, an EO object is built by assembling together Evolutionary Operators in an algorithm instance, and then calling the algorithm's operator() on an initial population (an eoPop). The algorithm will then manipulate the solutions within the population to search for the problem's optimum.