Desempenho do algoritmo genético com iteração retroviral para otimização de funções com representação real - FRANCO, Dielle da Silva Corrêa (Universidade Federal do Pará, 2017)
Viral Infection is used to improve the performance in Genetic Algorithms (GA) by reducing premature convergence through the population diversity control, since viruses presents high replication and mutation rates in the nature. The metaheuristic called AGRI is inspired biologicaly in a viruses family based on RNA, which provide a high allelic variation to GA, since RNA doesn’t have genoma correction mechanisms to remove re-combined viral genetic material . In this algorithm, the viruses are a separate population. To each infection, the better performance viruses genomes are transmitted vertically spreading parts of solutions to GA population. The diversity viral is maintained through a mechanism that substitutes all viruses out of elitism viral rate. In this method, the virus population evolves along with GA population, so the inefficient viruses are created from genetic material of the better adapted individuals and other new genes. The algorithm AGRI follows biological principles in several viral infection and multiplication aspects. For example: it creates the first viral population without GA population genetic material; it sorts the viral population before infect an individual, making possible some viruses doesn’t infected a part of the population and other viruses infect more individuals. Since GA second-generation, the replaced viruses are created by both individuals genetic material and have different genes quantities. In this approach, the search space maximization is increased by three mechanisms: high viral population genetic variability by variety of sizes to solutions pieces; infection validation process that confirms the fitness increases in each individual and infection possibility by any viruses in the viral population. To analyse the AGRI’s viral infection parameters effects and comparate his performance with others high-performing metaheuristics, the following minimization benchmarking are selected: F1 (Shifted Sphere Function), F2 (Shifted Schwefel’s Problem), F3 (Shifted Rotated High Conditioned Elliptic Function) e F5 (Schwefel’s Problem 2.6 with Global Optimum on Bounds). The results to the functions unimodais proposed showed that AGRI has a good performance in comparison with others metaheuristics reaching in few iterations the global best or good results.