EvolvingObjects
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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... |
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.