EvolvingObjects

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 populationtransforming algorithms. More...  
class  eoCellularEasyEA< EOT > 
The abstract cellular easy algorithm. More...  
class  eoEasyEA< EOT > 
An easytouse evolutionary algorithm; you can use any chromosome, and any selection transformation, merging and evaluation algorithms; you can even change in runtime parameters of those subalgorithms. More...  
class  eoEasyPSO< POT > 
An easytouse 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 easytouse synchronous particle swarm algorithm; you can use any particle, any flight, any topology... More... 
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.