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Analysis of Parameter Settings for the Bat Algorithm Using Variance Evolution
Xin-She Yang, Mehmet Karamanoglu
Parameter settings in evolutionary algorithms and metaheuristics are important because such parameter values can influence the performance of algorithms under evaluation. For a giv…
A Generalized Evolutionary Metaheuristic (GEM) Algorithm for Engineering Optimization
Xin-She Yang
Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple comple…
Parameter Tuning of the Firefly Algorithm by Standard Monte Carlo and Quasi-Monte Carlo Methods
Geethu Joy, Christian Huyck, Xin-She Yang
Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can significantly influence the behavior of the algorithm under con…
Nature-Inspired Algorithms in Optimization: Introduction, Hybridization and Insights
Xin-She Yang
Many problems in science and engineering are optimization problems, which may require sophisticated optimization techniques to solve. Nature-inspired algorithms are a class of meta…
A Nature-Inspired Feature Selection Approach based on Hypercomplex Information
Gustavo H. de Rosa, João Paulo Papa, Xin-She Yang
Feature selection for a given model can be transformed into an optimization task. The essential idea behind it is to find the most suitable subset of features according to some cri…
COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking
Eneko Osaba, Javier Del Ser, Xin-She Yang +2
Multitasking optimization is an emerging research field which has attracted lot of attention in the scientific community. The main purpose of this paradigm is how to solve multiple…