Randomized algorithms
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Theoretical analysis of diversity mechanisms for global exploration
Proceedings of the 10th annual conference on Genetic and evolutionary computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Analysis of the (1 + 1)-EA for finding approximate solutions to vertex cover problems
IEEE Transactions on Evolutionary Computation
Analysis of computational time of simple estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
The use of tail inequalities on the probable computational time of randomized search heuristics
Theoretical Computer Science
Unpacking and understanding evolutionary algorithms
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Bounding the population size of IPOP-CMA-ES on the noiseless BBOB testbed
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
| Hi-index | 5.23 |
The utilization of populations is one of the most important features of evolutionary algorithms (EAs). There have been many studies analyzing the impact of different population sizes on the performance of EAs. However, most of such studies are based on computational experiments, except for a few cases. The common wisdom so far appears to be that a large population would increase the population diversity and thus help an EA. Indeed, increasing the population size has been a commonly used strategy in tuning an EA when it did not perform as well as expected for a given problem. He and Yao (2002) [8] showed theoretically that for some problem instance classes, a population can help to reduce the runtime of an EA from exponential to polynomial time. This paper analyzes the role of population further in EAs and shows rigorously that large populations may not always be useful. Conditions, under which large populations can be harmful, are discussed in this paper. Although the theoretical analysis was carried out on one multimodal problem using a specific type of EAs, it has much wider implications. The analysis has revealed certain problem characteristics, which can be either the problem considered here or other problems, that lead to the disadvantages of large population sizes. The analytical approach developed in this paper can also be applied to analyzing EAs on other problems.