In this paper we present a new mutation procedure for Evolutionary Programming (EP) approaches, based on Iterated Function Systems (IFSs).
What one means by "explained by mutation" needs to be fleshed out, because not even the most dedicated proponent of a selectionist model of evolution will deny that mutations are necessary for evolutionary change.
In any natural population, mutation is the primary source of genetic variation required for evolutionary novelty and adaptation.
It has been retained without major mutations for a long evolutionary time.
In the last three years, next generation sequencing has been used for mapping of epigenetic mutations [ 19], identification of spontaneous mutations [ 20], or for evolutionary considerations [ 21] in yeast.
This paper presents a hybrid Particle Swarm Optimization based approach for fuzzy classifier design which incorporates the concept of mutation from evolutionary computations.
Finally, for each run we also recorded the total number of mutations tried as a surrogate for evolutionary time.
Since mycobacteria have a restricted synonymous polymorphism rate [15], this implies that deletion or acquisition of the genes might be a more important mechanism than point mutations for generating niche specific evolutionary novelties.
Non-adaptive explanations posit that extracellular proteins experience relaxed selection, and are more tolerant of mutations for structural, ecological, or evolutionary reasons [ 8, 9].
For this aim, we propose new mutation, crossover and reparation operators for evolutionary multi-objective optimization, which have been specially designed for generating feasible solutions of the cardinality constrained MDRS problem.
