Properties of a genetic algorithm extended by a random self-learning operator and asymmetric mutations: A convergence study for a task of powder-pattern indexing

摘要

Genetic algorithms represent a powerful global-optimisation tool applicable in solving tasks of high complexity in science,technology,medicine,communication,etc.The usual genetic-algorithm calculation scheme is extended here by introduction of a quadratic self-learning operator,which performs a partial local search for randomly selected representatives of the population.This operator is aimed as a minor deterministic contribution to the(stochastic)genetic search.The population representing the trial solutions is split into two equal subpopulations allowed to exhibit different mutation rates(so called asymmetric mutation).The convergence is studied in detail exploiting a crystallographic-test example of indexing of powder diffraction data of orthorhombic lithium copper oxide,varying such parameters as mutation rates and the learning rate.It is shown through the averaged(over the subpopulation)fitness behaviour,how the genetic diversity in the population depends on the mutation rate of the given subpopulation.Conditions and algorithm parameter values favourable for convergence in the framework of proposed approach are discussed using the results for the mentioned example.Further data are studied with a somewhat modified algorithm using periodically varying mutation rates and a problem-specific operator.The chance of finding the global optimum and the convergence speed are observed to be strongly influenced by the effective mutation level and on the self-learning level.The optimal values of these two parameters are about 6 and 5%,respectively.The periodic changes of mutation rate are found to improve the explorative abilities of the algorithm.The results of the study confirm that the applied methodology leads to improvement of the classical genetic algorithm and,therefore,it is expected to be helpful in constructing of algorithms permitting to solve similar tasks of higher complexity.