Really filling out the experiment with knobs to turn, getting better

Experiment.jl

@@ -4,6 +4,8 @@ import DataFrames
 POPSIZE = 5
 mu = 1
 lambda = 4
+GENLIMIT = 20
+TARGETCALORIES = 1000
 
 data = ExcelReaders.readxlsheet("./data/nutrional_information_5917.xlsx", "Sheet2", skipstartrows=1)
 header = ExcelReaders.readxlsheet("./data/nutrional_information_5917.xlsx", "Sheet2", nrows=1)
@@ -22,20 +24,21 @@ for i = 1:length(header)
     df[header[i]] = data[2:end, i]
 end
 
-function breeder(parent)
+function mutate(parent)
+    randomCandidate(4)
 end
 
+"""
+    fitness(candidate::DataFrames.DataFrame)
+
+Calculate the fitness of the candidate, which is the
+absolute value of the difference of TARGETCALORIES and the sum
+of all calories in the meal.
+"""
 function fitness(candidate::DataFrames.DataFrame)
-    sum(+, candidate[:Calories])
+    abs(TARGETCALORIES - sum(+, candidate[:Calories]))
 end
 
-function breed(candidates::Array{DataFrames.DataFrame})
-    # Truncation selection, top 3 as parents.
-    # First, check everyone's fitness.  
-    # Then, generate new solutions by selecting parents and breeding
-    sort!(candidates, by = x -> fitness(x))
-    parents = candidates[1:3]
-end
 
 function randRow()
     # Generate a random row index
@@ -48,8 +51,43 @@ function randomCandidate(n::Integer)
     df[rows, :]
 end
 
-function generateInitialPopulation()
+function generateInitialPopulation(lambda::Integer, candidateSize::Integer)
-    [randomCandidate(5) for i = 1:POPSIZE]
+    [randomCandidate(candidateSize) for i = 1:lambda]
 end
 
+function main()
+    # Generate the initial population.
+    pop = generateInitialPopulation(lambda, 4)
+    best = nothing
+    generationNum = 0
+    fit = nothing
+    parents = nothing
+
+    while generationNum != GENLIMIT || (best != nothing && fitness(best) != 0)
+
+        # Assess the fitness of parents
+        for parent in pop
+            fit = fitness(parent)
+            if best === nothing || fit < fitness(best)
+                best = parent
+            end
+        end
+
+        bestFitness = fitness(best)
+
+        sort!(pop, by = x -> fitness(x))
+        parents = pop[1:mu]
+        pop = parents
+
+        for p in parents
+            for i = 1:(lambda/mu)
+                push!(pop, mutate(p))
+            end
+        end
+
+        println("Generation $generationNum, best $best, fitness $bestFitness")
+        generationNum += 1
+    end
+
+end
 # search(generateInitialPopulation())