From understanding genetic drift to a smart-restart parameter-less compact genetic algorithm
Benjamin Doerr, Weijie Zheng
Abstract
One of the key difficulties in using estimation-of-distribution algorithms is choosing the population sizes appropriately: Too small values lead to genetic drift, which can cause enormous difficulties. In the regime with no genetic drift, however, often the runtime is roughly proportional to the population size, which renders large population sizes inefficient.
Topics & Concepts
Genetic algorithmGenetic driftPopulationComputer scienceKey (lock)Population sizeEstimation of distribution algorithmAlgorithmQuality control and genetic algorithmsGenetic representationPopulation-based incremental learningMathematical optimizationMathematicsMeta-optimizationMachine learningGenetic variationComputer securityDemographySociologyMetaheuristic Optimization Algorithms ResearchData Stream Mining TechniquesGene Regulatory Network Analysis