Proceedings of the Genetic and Evolutionary Computation Conference · 2019 · 30 citations · 14 references
Breeding BehaviorPopulation SizeFitnessGeneticsNatural SelectionComputational ComplexityEvolutionary AlgorithmsExponential RuntimeReproductive BiologyEvolution StrategyBreedingGenetic AlgorithmPublic HealthPopulation ControlEvolution-based MethodReproductive SuccessGenetic VariationPopulation GeneticsEvolutionary ProgrammingBiologyEvolutionary DynamicsEvolutionary BiologyEfficiency ThresholdMedicine
Understanding when evolutionary algorithms are efficient or not, and how they efficiently solve problems, is one of the central research tasks in evolutionary computation. In this work, we make progress in understanding the interplay between parent and offspring population size of the (µ, λ) EA. Previous works, roughly speaking, indicate that for λ ≥ (1 + ε)eµ, this EA easily optimizes the OneMax function, whereas an offspring population size λ ≤ (1 - ε)eµ leads to an exponential runtime.
14