Implementation of a Simple Binary Genome hill climber. The fitness is determined simply by how many 1's are turned on in the genome of the child. So a perfect fitness is 100.

hillClimber.go

package main

import (
	"fmt"	
	"math/rand"
	"time"
	"runtime"
)

/* HillClimber 
 * By Ethan J Eldridge.
 * Keep the population size great than 25 or you might have issues.
*/

var populationSize int; //How many there are
var genomeSize int;  	//How big their genome is
var generations int; 	//How many generations to run

type Child struct{
	genome []int
	resultChan chan int
	fitness int
}

func (c* Child) init(resChan * chan int) {
	c.resultChan = *resChan
	c.genome = make([]int, genomeSize)
	initilizeChannel := make(chan int, genomeSize)
	for i := 0; i < genomeSize; i++ {
		i := i //overshadow local 
		go func(){
			c.genome[i] = rand.Intn(2)
			initilizeChannel <- c.genome[i]
		}()	
	}
	//Compute initial fitness from initalization
	c.fitness = 0
	for i := 0; i < genomeSize; i++ {
		c.fitness += <- initilizeChannel
	}
}

func (c* Child) CopyTo(o * Child){
	o.genome = c.genome
	o.fitness = c.fitness
	o.resultChan = c.resultChan
}

func (c* Child) Mutate() {
	doneChannel := make(chan int, genomeSize)
	for i := 0; i < genomeSize; i++ {
		i := i
		go func(){
			if rand.Intn(2) == 1 {
				c.genome[i] = rand.Intn(2)
			}
			doneChannel <- c.genome[i]
		}()
	}
	c.fitness = 0
	for i := 0; i < genomeSize; i++ {
		c.fitness += <- doneChannel
	}
	//Send fitness out
	c.resultChan <- c.fitness
}


func main() {
	fmt.Printf("Simple HillClimber by Ethan J Eldridge\n  SettingUsing %v CPU's\n", runtime.NumCPU())
	runtime.GOMAXPROCS(runtime.NumCPU()) //use all the cpus!
	populationSize = 100
	genomeSize = 100
	generations = 50
	rand.Seed(time.Now().Unix())
	children := make([]Child, populationSize)
	resultsChan := make(chan int, populationSize)
	fmt.Printf("  Initializing Population...\n")
	for i := 0; i < populationSize; i++ {
		i := i
		go func(){
			children[i].init(&resultsChan)
			resultsChan <- children[i].fitness
		}()
	}
	bestFitness := 0
	bestIdx := 0
	for i := 0; i < populationSize; i++ {
		r := <-resultsChan	
		if r > bestFitness {
			bestIdx = i
			bestFitness = r
		}
	}
	fmt.Printf("  Population Ready, best child fitness: %v\n", bestFitness)
	for i := 0; i < generations; i++ {
		fmt.Printf("  Running Generation %v\n", i)
		for j := 0; j < populationSize; j++ {
			j := j
			go func(){
				children[j].Mutate()
			}()
		}
		//Collect results of population mutation
		for j := 0; j < populationSize; j++ {
			tmpFitness := <- resultsChan
			if tmpFitness > bestFitness {
				bestFitness = tmpFitness
				bestIdx = j
			}
		}
		fmt.Printf("    Best Of Generation %v : Child: %v, fitness: %v\n", i, bestIdx,bestFitness)
		//'Breed' new results from the best child
		if bestIdx < populationSize/2 {
			for i := bestIdx+1; i < bestIdx + 11; i++ {
				children[bestIdx].CopyTo( &children[i] )
				children[i].Mutate()
				<- resultsChan //discard the mutated value
			}
		} else {
			for i := bestIdx-11; i < bestIdx; i++ {
				children[bestIdx].CopyTo( &children[i] )
				children[i].Mutate()
				<- resultsChan //discard the mutated value
			}
		}
	}
	fmt.Printf("  Overall Best: %v with %v fitness score\n", bestIdx, bestFitness)


}