Evolutionary Algorithms: The Role of Mutation and Recombination
Evolutionary Algorithms: The Role of Mutation and Recombination
A review of adaptive population sizing schemes in genetic algorithms
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Dynamically tuning the population size in particle swarm optimization
Proceedings of the 2008 ACM symposium on Applied computing
Evolutionary based heuristic for bin packing problem
Computers and Industrial Engineering
Improving genetic algorithms performance via deterministic population shrinkage
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An exploration into dynamic population sizing
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Pattern Recognition Letters
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In an evolutionary algorithm, the population has a very important role as its size has direct implications regarding solution quality, speed, and reliability. Theoretical studies have been done in the past to investigate the role of population sizing in evolutionary algorithms. In addition to those studies, several self-adjusting population sizing mechanisms have been proposed in the literature. This paper revisits the latter topic and pays special attention to the genetic algorithm with adaptive population size (APGA), for which several researchers have claimed to be very effective at autonomously (re)sizing the population.As opposed to those previous claims, this paper suggests a complete opposite view. Specifically, it shows that APGA is not capable of adapting the population size at all. This claim is supported on theoretical grounds and confirmed by computer simulations.