Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
Journal of Computer Science and Technology
Some theoretical results about the computation time of evolutionary algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
Reference chromosome to overcome user fatigue in IEC
New Generation Computing
Portrait beautification: A fast and robust approach
Image and Vision Computing
Editorial: Special Issue on "Nature Inspired Problem-Solving"
Information Sciences: an International Journal
About the Time Complexity of Evolutionary Algorithms Based on Finite Search Space
Computational Intelligence and Security
About the Computation Time of Adaptive Evolutionary Algorithms
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Real options approach to evaluating genetic algorithms
Applied Soft Computing
On average time complexity of evolutionary negative selection algorithms for anomaly detection
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Convergence analysis of UMDAC with finite populations: a case study on flat landscapes
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A pheromone-rate-based analysis on the convergence time of ACO algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
When is an estimation of distribution algorithm better than an evolutionary algorithm?
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Genetic algorithms on NK-landscapes: effects of selection, drift, mutation, and recombination
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Viewing the problem from different angles: a new diversity measure based on angular distances
Journal of Artificial Evolution and Applications
Information Sciences: an International Journal
Analysis of (1+1) evolutionary algorithm and randomized local search with memory
Evolutionary Computation
The convergence of a multi-objective evolutionary algorithm based on grids
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
A time complexity analysis of ACO for linear functions
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
An experimental comparative study for interactive evolutionary computation problems
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Convergence analysis and improvements of quantum-behaved particle swarm optimization
Information Sciences: an International Journal
Convergence of a recombination-based elitist evolutionary algorithm on the royal roads test function
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
A large population size can be unhelpful in evolutionary algorithms
Theoretical Computer Science
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
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Almost all analyses of time complexity of evolutionary algorithms (EAs) have been conducted for (1 + 1) EAs only. Theoretical results on the average computation time of population-based EAs are few. However, the vast majority of applications of EAs use a population size that is greater than one. The use of population has been regarded as one of the key features of EAs. It is important to understand in depth what the real utility of population is in terms of the time complexity of EAs, when EAs are applied to combinatorial optimization problems. This paper compares (1 + 1) EAs and (N + N) EAs theoretically by deriving their first hitting time on the same problems. It is shown that a population can have a drastic impact on an EA's average computation time, changing an exponential time to a polynomial time (in the input size) in some cases. It is also shown that the first hitting probability can be improved by introducing a population. However, the results presented in this paper do not imply that population-based EAs will always be better than (1 + 1) EAs for all possible problems