Drift analysis and average time complexity 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
Neural-Based Learning Classifier Systems
IEEE Transactions on Knowledge and Data Engineering
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
When is an estimation of distribution algorithm better than an evolutionary algorithm?
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Analysis of the (1 + 1)-EA for finding approximate solutions to vertex cover problems
IEEE Transactions on Evolutionary Computation
Memetic algorithm with extended neighborhood search for capacitated arc routing problems
IEEE Transactions on Evolutionary Computation
Analysis of computational time of simple estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Population-based algorithm portfolios for numerical optimization
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Software Module Clustering as a Multi-Objective Search Problem
IEEE Transactions on Software Engineering
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Crossover can be constructive when computing unique input–output sequences
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Optimization and Learning
Scalability of generalized adaptive differential evolution for large-scale continuous optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
On the approximation ability of evolutionary optimization with application to minimum set cover
Artificial Intelligence
Evolutionary Design of Digital Filters With Application to Subband Coding and Data Transmission
IEEE Transactions on Signal Processing
Evolving artificial neural network ensembles
IEEE Computational Intelligence Magazine
Robust route optimization for gritting/salting trucks: a CERCIA experience
IEEE Computational Intelligence Magazine
A large population size can be unhelpful in evolutionary algorithms
Theoretical Computer Science
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Measuring Generalization Performance in Coevolutionary Learning
IEEE Transactions on Evolutionary Computation
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Theoretical analysis of evolutionary algorithms (EAs) has made significant progresses in the last few years. There is an increased understanding of the computational time complexity of EAs on certain combinatorial optimisation problems. Complementary to the traditional time complexity analysis that focuses exclusively on the problem, e.g., the notion of NP-hardness, computational time complexity analysis of EAs emphasizes the relationship between algorithmic features and problem characteristics. The notion of EA-hardness tries to capture the essence of when and why a problem instance class is hard for what kind of EAs. Such an emphasis is motivated by the practical needs of insight and guidance for choosing different EAs for different problems. This chapter first introduces some basic concepts in analysing EAs. Then the impact of different components of an EA will be studied in depth, including selection, mutation, crossover, parameter setting, and interactions among them. Such theoretical analyses have revealed some interesting results, which might be counter-intuitive at the first sight. Finally, some future research directions of evolutionary computation will be discussed.