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
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
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
On the Optimization of Unimodal Functions with the (1 + 1) Evolutionary Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
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
How mutation and selection solve long-path problems in polynomial expected time
Evolutionary Computation
Rigorous hitting times for binary mutations
Evolutionary Computation
A new approach to estimating the expected first hitting time of evolutionary algorithms
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
An evolutionary clustering algorithm for gene expression microarray data analysis
IEEE Transactions on Evolutionary Computation
A new query reweighting method for document retrieval based on genetic algorithms
IEEE Transactions on Evolutionary Computation
Rigorous time complexity analysis of univariate marginal distribution algorithm with margins
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Analysis of computational time of simple estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Towards analyzing recombination operators in evolutionary search
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
An analysis on recombination in multi-objective evolutionary optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On the approximation ability of evolutionary optimization with application to minimum set cover
Artificial Intelligence
Convergence analysis and improvements of quantum-behaved particle swarm optimization
Information Sciences: an International Journal
The use of tail inequalities on the probable computational time of randomized search heuristics
Theoretical Computer Science
On algorithm-dependent boundary case identification for problem classes
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
An analysis on recombination in multi-objective evolutionary optimization
Artificial Intelligence
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Evolutionary algorithms (EA) have been shown to be very effective in solving practical problems, yet many important theoretical issues of them are not clear. The expected first hitting time is one of the most important theoretical issues of evolutionary algorithms, since it implies the average computational time complexity. In this paper, we establish a bridge between the expected first hitting time and another important theoretical issue, i.e., convergence rate. Through this bridge, we propose a new general approach to estimating the expected first hitting time. Using this approach, we analyze EAs with different configurations, including three mutation operators, with/without population, a recombination operator and a time variant mutation operator, on a hard problem. The results show that the proposed approach is helpful for analyzing a broad range of evolutionary algorithms. Moreover, we give an explanation of what makes a problem hard to EAs, and based on the recognition, we prove the hardness of a general problem.