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

 

Renato Simões Moreira¹, Otávio Noura Teixeira², and Roberto Célio Limão de Oliveira¹

 

¹ PPGEE–ITEC, Universidade Federal do Pará (UFPA), Belém, PA, Brasil. (renatosm@gmail.com; limao@ufpa.br).

² Laboratório de Computação Natural (LCN), Centro Universitário do Pará (CESUPA), Belém, PA, Brasil; Movimento Evolucionário e Cooperativo para a Construção do Artificial (MEC2A), Belém, PA, Brasil; PPGEE–ITEC, Universidade Federal do Pará (UFPa), Belém, PA, Brasil. (onoura@gmail.com).

 

Manuscript received May 7, 2010.
Manuscript accepted for publication August 29, 2010.

 

Abstract

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.

Key words: Evolutionary computation, genetic algorithm, viruses, retroviruses, hybrid metaheuristic.

 

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