This paper presents a guided and deep introduction to viral systems (VS), a novel bio-inspired methodology based on a natural biological process taking part when the organism has to give a response to an external infection. VS has proven to be very efficient when dealing with problems of high complexity. The paper discusses on the foundations of VS, presents the main pseudocodes that need to be implemented and illustrates the methodology application. A comparison between VS and other metaheuristics, as well between different VS approaches is presented. Finally, trends and new research opportunities are presented for this bio-inspired methodology.
CortésPGarcíaJMMuñuzuriJGuadixJ (2010) A viral system massive infection algorithm to solve the Steiner tree problem in graphs with medium terminal density. International Journal of Bio-inspired Computation2(2): 71–7.
3.
CortésPGarcíaJMMuñuzuriJOnievaL (2008) Viral systems: a new bio-inspired optimisation approach. Computers & Operations Research35(9): 2840–60.
4.
CutelloVNicosiaGPavoneM (2007a) An immune algorithm with stochastic aging and Kullback entropy for the chromatic number problem. Journal of Combinatorial Optimization14(1): 9–33.
5.
CutelloVNicosiaGPavoneMTimmisJ (2007b) An immune algorithm for protein structure prediction on lattice models. IEEE Transaction on Evolutionary Computation11(1): 101–17.
6.
GabrelV (1995) Scheduling jobs within time windows on identical parallel machines: new model and algorithms. European Journal of Operational Research83: 320–9.
7.
GertsbakhISternH (1978) Minimal resources for fixed and variable job schedules. Operations Research26(1): 68–85.
8.
KovalyovMNgCChengT (2007) Fixed interval scheduling: models, applications, computational complexity and algorithms. European Journal of Operational Research178: 331–42.
9.
KubotaNFukudaTShimojimaK (1996) Virus-evolutionary genetic algorithm for a self-organizing manufacturing system. Computers and Industrial Engineering30(4): 1015–26.
10.
SaitoS (2003) A genetic algorithm by use of virus evolutionary theory for combinatorial problems. Optimization and Optimal ControlVol. 1, 10, Singapore: World Scientific Publishing Co., 251–68.
11.
Van Dyke ParunakH (1997) Go to the ant: engineering principles from natural multi-agent systems. Annals of Operations Research75: 69–101.
12.
WolfeWJSorensenSE (2000) Three scheduling algorithms applied to Earth observing systems domain. Management Science46: 148–68.
