go

README.md

@@ -13,7 +13,7 @@ This repository contains a simple implementation of a genetic algorithm for  evo
 
 In order to use this, we first need to create a "phenotype" representation which contains the dna `[]byte`. It should implement the `Evolver` interface which contains `Genome()` and `Evolve()` methods, in the example here we are creating a simple text which contains the binary representation of the text itself.
 
-```
+```go
 // Text represents a text with a dna (text itself in this case)
 type text struct {
 	dna []byte
@@ -35,8 +35,7 @@ func (t *text) String() string {
 }
 ```
 Next, we'll need a fitness function to evaluate how good a genome is. In this example we're creating a fitness function for an abritrary string which simply returns a `func(Evolver) float32`
-```
-
+```go
 // fitnessFor returns a fitness function for a string
 func fitnessFor(text string) evolve.Fitness {
 	target := []byte(text)
@@ -54,7 +53,7 @@ func fitnessFor(text string) evolve.Fitness {
 
 Finally, we can wire everything together by using `New()` function to create a population, and evolve it by repeatedly calling `Evolve()` method as shown below.
 
-```
+```go
 func main() {
 	const target = "Hello World"
 	const n = 200