Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Mixed-Integer evolution strategies and their application to intravascular ultrasound image analysis
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Mixed-Integer Evolution Strategies with Dynamic Niching
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
On the log-normal self-adaptation of the mutation rate in binary search spaces
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Mixed integer evolution strategies for parameter optimization
Evolutionary Computation
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NK landscapes (NKL) are stochastically generated pseudo-boolean functions with N bits (genes) and K interactions between genes. By means of the parameter K ruggedness as well as the epistasis can be controlled. NKL are particularly useful to understand the dynamics of evolutionary search. We extend NKL from the traditional binary case to a mixed variable case with continuous, nominal discrete, and integer variables. The resulting test function generator is a suitable test model for mixed-integer evolutionary algorithms (MI-EA) – i. e. instantiations of evolution algorithms that can deal with the aforementioned variable types. We provide a comprehensive introduction to mixed-integer NKL and characteristics of the model (global/local optima, computation, etc.). Finally, a first study of the performance of mixed-integer evolution strategies on this problem family is provided, the results of which underpin its applicability for optimization algorithm design.