A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Foundations of genetic algorithms
Foundations of genetic algorithms
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
How to detect all maxima of a function
Theoretical aspects of evolutionary computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
A Note on the Griewank Test Function
Journal of Global Optimization
Building Better Test Functions
Proceedings of the 6th International Conference on Genetic Algorithms
Computing separable functions via gossip
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Application domain study of evolutionary algorithms in optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Differential evolution strategy for structural system identification
Computers and Structures
The genetic algorithm for breast tumor diagnosis-The case of DNA viruses
Applied Soft Computing
Automated layout design of beam-slab floors using a genetic algorithm
Computers and Structures
Real-valued multimodal fitness landscape characterization for evolution
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
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Currently, researchers in the field of Evolutionary Algorithms (EAs) are very interested in competitions where new algorithm implementations are evaluated and compared. Usually, EA users perform their algorithm selection by following the results published in these competitions, which are typically focused on average performance measures over benchmark sets. These sets are very complete but the functions within them are usually classified into binary classes according to their separability and modality. Here we show that this binary classification could produce misleading conclusions about the performance of the EAs and, consequently, it is necessary to consider finer grained features so that better conclusions can be obtained about what scenarios are adequate or inappropriate for an EA. In particular, new elements are presented to study separability and modality in more detail than is usually done in the literature. The need for such detail in order to understand why things happen the way they do is made evident over three different EAs.