diff --git a/index.js b/index.js
index 76dde6e..1b69be8 100644
--- a/index.js
+++ b/index.js
@@ -10,6 +10,8 @@ module.exports = function geneticAlgorithmConstructor(options) {
 
         doesABeatBFunction : undefined,
  
+        randomFunction : function() { return Math.random() },
+
         population : [],
         populationSize : 100,
     }}
@@ -23,6 +25,8 @@ module.exports = function geneticAlgorithmConstructor(options) {
 
         settings.doesABeatBFunction = settings.doesABeatBFunction || defaults.doesABeatBFunction
 
+        settings.randomFunction = settings.randomFunction || defaults.randomFunction
+
         settings.population = settings.population || defaults.population
         if ( settings.population.length <= 0 ) throw Error("population must be an array and contain at least 1 phenotypes")
 
@@ -39,7 +43,7 @@ module.exports = function geneticAlgorithmConstructor(options) {
         while( settings.population.length < settings.populationSize ) {
             settings.population.push(
                 mutate(
-                    cloneJSON( settings.population[ Math.floor( Math.random() * size ) ] )
+                    cloneJSON( settings.population[ Math.floor( settings.randomFunction() * size ) ] )
                 )
             )
         }
@@ -55,7 +59,7 @@ module.exports = function geneticAlgorithmConstructor(options) {
 
     function crossover(phenotype) {
         phenotype = cloneJSON(phenotype)
-        var mate = settings.population[ Math.floor(Math.random() * settings.population.length ) ]
+        var mate = settings.population[ Math.floor(settings.randomFunction() * settings.population.length ) ]
         mate = cloneJSON(mate)
         return settings.crossoverFunction(phenotype,mate)[0]
     }
@@ -78,7 +82,7 @@ module.exports = function geneticAlgorithmConstructor(options) {
 
             nextGeneration.push(phenotype)
             if ( doesABeatB( phenotype , competitor )) {
-                if ( Math.random() < 0.5 ) {
+                if ( settings.randomFunction() < 0.5 ) {
                     nextGeneration.push(mutate(phenotype))
                 } else {
                     nextGeneration.push(crossover(phenotype))
@@ -96,7 +100,7 @@ module.exports = function geneticAlgorithmConstructor(options) {
     function randomizePopulationOrder( ) {
 
         for( var index = 0 ; index < settings.population.length ; index++ ) {
-            var otherIndex = Math.floor( Math.random() * settings.population.length )
+            var otherIndex = Math.floor( settings.randomFunction() * settings.population.length )
             var temp = settings.population[otherIndex]
             settings.population[otherIndex] = settings.population[index]
             settings.population[index] = temp
diff --git a/test/basic.js b/test/basic.js
index 4cf11f6..12959e9 100644
--- a/test/basic.js
+++ b/test/basic.js
@@ -98,6 +98,33 @@ module.exports = {
         assert.equal( true , ga.bestScore() > 1 , "Error : untrue : " + ga.bestScore() + " > 1");
 
 
-    }
+    },
+    'custom randomFunction works': function (beforeExit, assert) {
+      let customRandomCalled = false;
+      const customRandomFunction = () => {
+        customRandomCalled = true;
+        return Math.random();
+      };
+
+      var config = {
+        mutationFunction: function (phenotype) {
+          return phenotype;
+        },
+        crossoverFunction: function (a, b) {
+          return [a, b];
+        },
+        fitnessFunction: function (phenotype) {
+          return 0;
+        },
+        population: [{ name: "alice" }],
+        randomFunction: customRandomFunction,
+      };
+
+      var geneticalgorithm = geneticAlgorithmConstructor(config);
+
+      geneticalgorithm.evolve();
+      assert.equal(true, customRandomCalled, "customRandomFunction was not called");
+    },
+
 }