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