Hi,

I've created a parameters file.
verbosity    = 0

breedthreads = 1
evalthreads  = 1
seed.0       = 4357

state  = ec.simple.SimpleEvolutionState

pop    = ec.Population
init   = ec.simple.SimpleInitializer
finish = ec.simple.SimpleFinisher
breed  = ec.simple.SimpleBreeder
eval   = ec.simple.SimpleEvaluator
stat   = ec.simple.SimpleStatistics
exch   = ec.simple.SimpleExchanger

generations             = 1
quit-on-run-complete    = true
checkpoint              = false
prefix                  = ec
checkpoint-modulo       = 1

stat.file       = out.stat

pop.subpops     = 1
pop.subpop.0    = ec.Subpopulation

# The number of individuals(solutions) in a population. Size should be an even 
number to breed.
pop.subpop.0.size                  = 5
pop.subpop.0.duplicate-retries     = 0
# Doubles and floats use the FloatVectorSpecies
pop.subpop.0.species               = ec.vector.FloatVectorSpecies
pop.subpop.0.species.ind           = ec.vector.FloatVectorIndividual
pop.subpop.0.species.fitness       = ec.simple.SimpleFitness

# One-point crossover
pop.subpop.0.species.genome-size = 5
pop.subpop.0.species.crossover-type = one
pop.subpop.0.species.crossover-prob = 1.0
# Per-gene mutation probability of 1.0
pop.subpop.0.species.mutation-prob  = 1.0

pop.subpop.0.species.pipe = ec.vector.breed.VectorMutationPipeline
pop.subpop.0.species.pipe.source.0 = ec.vector.breed.VectorCrossoverPipeline
pop.subpop.0.species.pipe.source.0.source.0 = ec.select.TournamentSelection
pop.subpop.0.species.pipe.source.0.source.1 = ec.select.TournamentSelection

pop.subpop.0.species.min-gene = 0
pop.subpop.0.species.max-gene = 1
pop.subpop.0.file = pop.in

# Use Gaussian Convolution with a standard deviation of 0.1
pop.subpop.0.species.mutation-type = gauss
pop.subpop.0.species.mutation-stdev = 0.1

select.tournament.size = 2

eval.problem = weightOptimiser.WeightOptimiser

breed.elites.0 = 1

And created a pop.in file with the initial population that I want. 

Number of Individuals: i5|
Individual Number: i0|
Evaluated: F
Fitness: f0|0.0| 
f1056964608|0.5|f1056964608|0.5|f1056964608|0.5|f1056964608|0.5|f10569
64608|0.5|
Individual Number: i1|
Evaluated: F
Fitness: f0|0.0|
f0|0.0|f0|0.0|f0|0.0|f0|0.0|f0|0.0|
Individual Number: i2|
Evaluated: F
Fitness: f0|0.0|
f1065353216|1.0|f1065353216|1.0|f1065353216|1.0|f1065353216|1.0|f10653
53216|1.0|
Individual Number: i3|
Evaluated: F
Fitness: f0|0.0|
f1048576000|0.25|f1048576000|0.25|f1048576000|0.25|f1048576000|0.25|f1
048576000|0.25|
Individual Number: i4|
Evaluated: F
Fitness: f0|0.0|
f1061158912|0.75|f1061158912|0.75|f1061158912|0.75|f1061158912|0.75|f1
061158912|0.75|


These numbers were generated using the Code class.

		System.out.println(Code.encode(0.5f));
		System.out.println(Code.encode(0.0f));
		System.out.println(Code.encode(1.0f));
		System.out.println(Code.encode(0.25f));
		System.out.println(Code.encode(0.75f));


This is my evaluate method.

public void evaluate(final EvolutionState state, final Individual ind,
			final int subpopulation, final int threadnum) {
			
		if(ind.evaluated)
			return;
		
		if( !( ind instanceof FloatVectorIndividual ) )
            state.output.fatal( "The individuals for this problem should be 
FloatVectorIndividuals." );
		
		
		ArrayList<Float> weights = new ArrayList<Float>();
		float fitnessValue = 0.00f;
		float[] genome = ((FloatVectorIndividual)ind).genome;

		fitnessValue = AssessClustersFitness.calculateFitness();

		SimpleFitness fitness = (SimpleFitness) ind.fitness;
		fitness.setFitness(state, fitnessValue, fitnessValue==1.00);
		
		
		ind.evaluated = true;
	}

But when I run it the "genome" array is empty instead of being populated with 
the initial values. Am I missing something here?

Thanks.
Rui