Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Finite Markov chain results in evolutionary computation: a tour d'horizon
Fundamenta Informaticae
On the convergence rates of genetic algorithms
Theoretical Computer Science - Special issue on evolutionary computation
Statistical dynamics of the Royal Road genetic algorithm
Theoretical Computer Science - Special issue on evolutionary computation
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Conditions for the convergence of evolutionary algorithms
Journal of Systems Architecture: the EUROMICRO Journal - Special issue on evolutionary computing
Computer Algorithms: Introduction to Design and Analysis
Computer Algorithms: Introduction to Design and Analysis
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
Rigorous hitting times for binary mutations
Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A new approach to estimating the expected first hitting time of evolutionary algorithms
Artificial Intelligence
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
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The expected first hitting time is an important issue in theoretical analyses of evolutionary algorithms since it implies the average computational time complexity. In this paper, by exploiting the relationship between the convergence rate and the expected first hitting time, a new approach to estimating the expected first hitting time is proposed. This approach is then applied to four evolutionary algorithms which involve operators of mutation, mutation with population, mutation with recombination, and time-variant mutation, respectively. The results show that the proposed approach is helpful for analyzing a broad range of evolutionary algorithms.