The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experime...

this study is focused on the development of a systematic computational approach which implements genetic algorithm (ga) to find the optimal rigorous kinetic models.a general kinetic model for hydrogenolysis of dibenzothiophene (dbt) based on langmuir-hinshelwood type has been obtained from open literature. this model consists of eight continuous parameters(e.g., arrhenus  and van't hoff paramet...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...

In this paper, we discuss on new Coevolutionary Genetic Algorithm for Constraint Satisfaction. Our basic idea is to explore effective genetic information in the population, i.e., schemata, and to exploit the genetic information in order to guide the population to better solutions. Our Coevolutiona,ry Genetic Algorithm (CGA) consists of two GA populations; the first GA, called ”H-GA”, searches f...

Genetic Algorithm (GA) is known as method to solve Traveling Salesman Problem (TSP). However, GA needs amount of time for finding approximate solution. Thus, this study proposes Genetic Algorithm with Virus Infection (GAVI). GAVI algorithm is using Virus Theory of Evolution (VTE) to be based on Genetic Algorithm (GA). GA can evolve by natural selection, while VTE can evolve by virus infection. ...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...