doc.go

// Genetic problem solver
//
// This genetic solver adaptively adjusts the strategies being used to those that are most successful at finding solutions to your problem.
//
// Usage:
//
// GeneticGo is compatible with Go 1. Add it to your package repository:
//
//     go get "github.com/handcraftsman/Random"
//     go get "github.com/handcraftsman/GeneticGo"
//
// then use it in your program:
// 
//     import "github.com/handcraftsman/GeneticGo"
//     ...
//     solver := new(genetic.Solver)
//     solver.MaxSecondsToRunWithoutImprovement = 20 // you decide
//     solver.LowerFitnessesAreBetter = true // you decide
// 	
// Create a fitness function:
//
// Return a negative value if the sequence is invalid, otherwise
// return a value that approaches 0 or MaxInt32 as it gets better
// depending on what's best for your problem.
// When hillclimbing, fitness values that are closer to the optimal
// value are assumed to be better, even if higher than the optimal.
// 
//     getFitness := func(candidate string) int {
//         return ?? // evaluate the candidate and return a fitness value
//     }
// 	
// 
// create a display function
// 
//     display := func(genes string) {
//         println(??) // provide some output to the user if desired
//     }
// 	
// each gene is a single character
//     geneSet := "abc123..." // you decide the set of valid genes
//     numberOfGenesInAChromosome := 1 // you decide
// 	
//     solver.NumberOfConcurrentEvolvers = 4 // you decide, defaults to 1
//     solver.MaxProcs // you decide, defaults to 1
//
// if your problem can be solved with a fixed number of genes:
// 
//     numberOfChromosomes := 10 // you decide
//     var result = solver.GetBest(getFitness, display, geneSet, numberOfChromosomes, numberOfGenesInAChromosome)
// 
// alternatively, if you want the gene sequence to grow as necessary:
// 
//     solver.MaxRoundsWithoutImprovement = 10 // you decide
//     bestPossibleFitness := 0 // you decide
//     maxNumberOfChromosomes := 50 // you decide
// 	
//     var result = solver.GetBestUsingHillClimbing(getFitness, display, geneSet, maxNumberOfChromosomes, numberOfGenesInAChromosome, bestPossibleFitness)
//
// see the samples directory for specific examples
package genetic