Extending standard evolutionary programming with self-adaptive stable distributions. Issue 2 (3rd March 2016) - Carvalho, Leopoldo B.; de Oliveira, Pedro P.B.
Abstract : Applications in evolutionary programming have suggested the use of further stable probability distributions, such as Cauchy and Lévy, in the random process associated with the mutations, as an alternative to the traditional, also stable, normal distribution. This work goes further along the encouraging results of the latter, by extending them in a self-adaptive way, with algorithms that are in tune with the standard lineage of evolutionary programming. Evaluations that rely upon standard analytical benchmarking functions and comparative performance tests between them were carried out in respect to the baseline defined by the standard evolutionary programming algorithm that relies on normal distribution. Additional comparative studies were made in respect to various self-adaptive approaches, also proposed herein, and a method drawn from the literature. The results lead to numerical and statistical superiority of the more general stable distribution based approach, when compared with the baseline, and is unclear in regard to the method drawn from the literature, possibly due to distinct implementation details.