Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
A Note on the Extended Rosenbrock Function
Evolutionary Computation
A parameter-less evolution strategy for global optimization
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Exploring macroevolutionary algorithms: some extensions and improvements
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Macroevolutionary algorithms: a new optimization method on fitnesslandscapes
IEEE Transactions on Evolutionary Computation
Are evolutionary algorithm competitions characterizing landscapes appropriately
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Evolutionary algorithm characterization in real parameter optimization problems
Applied Soft Computing
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This paper deals with the problem of comparing and testing evolutionary algorithms, that is, the benchmarking problem, from an analysis point of view. A practical study of the application domain of four representative evolutionary algorithms is carried out using a relevant set of real-parameter function optimization benchmarks. The four selected algorithms are the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the Differential Evolution (DE), due to their successful results in recent studies, a Genetic Algorithm with real parameter operators, used here as a reference approach because it is probably the most familiar to researchers, and the Macroevolutionary algorithm (MA), which is not widely known but it shows a very remarkable behavior in some problems. The algorithms have been compared running several tests over the benchmark function set to analyze their capabilities from a practical point of view, in other words, in terms of their usability. The characterization of the algorithms is based on accuracy, stability and time consumption parameters thus establishing their operational scope and the type of optimization problems they are more suitable for.