Today, doctors performing in vitro fertilization use various techniques to check embryos for genetic problems, such as mistakes in chromosome number, before they are transferred to the uterus.
Wilkins went on to study the structure of various forms of RNA, a genetic material in its own right as well as an information messenger within cells, and a wide range of genetic problems, such as those of ageing, of genetic diseases and of obesity.
The multi-dimensionality and high genetic correlations create several problems such as over-parameterized models, increased sampling variances and an increased probability of parameters to be outside the boundaries of the parameter space, e.g. [ 2].
Recently, in literature, there has been a considerable attention paid to employing algorithms inspired by natural processes or events to solve optimization problems such as genetic algorithms, simulated annealing, ant colony optimization and particle swarm optimization.
Evolutionary algorithms for numerical optimization problems, such as a genetic algorithm for numerical problems (realGA), evolution strategies (ES) [ 17], particle swarm optimization (PSO) [ 18], and differential evolution (DE) [ 19] are capable of solving such optimization problems.
For instance, new insights may be gained by revisiting well-known problems, such as the genetic toggle switch [ 28], by considering the effects of wide ranges molecular diffusion in gene expression, in relevant simulation time spans.
These large-scale phylogenetic studies provide unique and valuable insights into the molecular and genetic basis for important medical and epidemiological problems such as drug resistance [1], molecular receptor function [2] as well as important questions about the origins and development of physiological features in present day organisms [3], [4].
Stochastic algorithms, developed for solving large scale combinatorial problems, such as ant colony optimization, genetic algorithms, and simulated annealing are also used [ 34- 36].
However, GWAS can suffer from problems such as confounding due to genetic background, or diminishing power to find associations for rare alleles [ 5].
To date, genetic engineering technology has been harnessed largely to address problems such as weeds and insects in major crops like maize and soybeans.
However, the rapid expansion of the aquaculture industry also brings with it problems such as pollution, outbreaks of disease, genetic deteriorations and seasonal oversupply etc. Aquaculture has been threatened by agricultural and industrial effluent, as well as the tourist industry.
