The problem I'm working on has gene strings that can vary from 50-500 fixed-range integers in length. I'm finding that we get pretty good results and convergence rate out of our GA with small genomes, but at the upper end of the range things go nowhere fast. Our GA is configured using the same pipeline and such as one of the example problems: pop.subpops = 1 pop.subpop.0 = ec.Subpopulation pop.subpop.0.size = 100 pop.subpop.0.duplicate-retries = 0 pop.subpop.0.fitness = ec.simple.SimpleFitness pop.subpop.0.species.crossover-type = one pop.subpop.0.species.crossover-prob = 0.8 pop.subpop.0.species.mutation-prob = 0.04 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.pipe.source.0.source.0.size = 2 pop.subpop.0.species.pipe.source.0.source.1.size = 2 select.tournament.size = 2 state = ec.simple.SimpleEvolutionState pop = ec.Population init = ec.simple.SimpleInitializer finish = ec.simple.SimpleFinisher breed = ec.simple.SimpleBreeder Min/max gene sizes and genome size are set at runtime. Any suggestions on what I can do to improve performance with very large genomes? Thanks, Sandeep