This study is a survey about how Evolutionary Computing doesn't play its important role in a vital field such as Nutrition. Evolutionary computing is a subset from the artificial intelligence umbrella that involves continuous optimization and combinational optimization which is based on searching methodologies. It has also a lot of algorithms that have played a main role in supporting the decision making and taking processes accurately and effectively. It is concerning many fields in our life such as Industry, Agriculture, Engineering, Transportation, Medicine and Nutrition, etc. One of these algorithms is Genetic Algorithm (GA) which is contributed to a lot of fields. Moreover, Nutrition is a wide field of research because it has several sides, medically, physically and psychologically and so on. But, has Genetic Algorithms been used to contribute to the field of nutrition? This survey illustrates that (GA) is not involved in nutrition computerized models or applications and it suggests building a model to promote a nutrition system using this powerful algorithm and this study presents a suggestion to build a model for nutrition as a future work that uses Genetic Algorithm.

Identification of major histocompatibility complex binding peptides is an important step in the selection of T-Cell epitope candidates suitable for usage in new vaccines.The binding groove of the MHC Class-II molecule is opened at both sides, which allows for high variability in length of the peptides that bind to this molecule and consequently complicates the prediction of the binding core motif. An accurate and efficient computational approach for the prediction of such peptides can greatly reduce the time and cost required for the design of new vaccines for infectious diseases and cancers. We have developed EpiGASVM, a new approach for the in silico prediction of MHC Class-II epitopes, by combining two artificial intelligence techniques namely: evolutionary algorithms and support vector machines. We have applied nine variations of EpiGASVM to a dataset of similarity-reduced benchmark data and we have calculated the prediction accuracy and the area under the receiver operating characteristic curve as measures of performance.The results indicate that Epi-GASVM is a promising new technique that could provide researchers with a new tool for the in silico selection of candidate peptides that can be used in rational vaccine design.