Mixing Theory of Retroviruses and Genetic Algorithm to Build a New Nature-Inspired Meta-Heuristic for Real-Parameter Function Optimization Problems

this paper describes the development of a new hybrid meta-heuristic of optimization based on a viral lifecycle, specifically the retroviruses (the nature's swiftest evolvers), called retroviral iterative genetic algorithm (riga). this algorithm uses genetics algorithms (ga) structures with features of retroviral replication, providing a great genetic diversity, confirmed by better results achieved by riga comparing with ga applied to some real-valued benchmarking functions.