Abstract
In this paper, we show the functional similarities between Meta-heuristics and the aspects of the science of life (biology): (a) Meta-heuristics based on gene transfer: Genetic algorithms (natural evolution of genes in an organic population), Transgenic Algorithm (transfers of genetic material to another cell that is not descending); (b) Meta-heuristics based on interactions among individual insects: Ant Colony Optimization (on interactions among individuals insects, Ant Colonies), Firefly algorithm (fireflies of the family Lampyridze), Marriage in honey bees Optimization algorithm (the process of reproduction of Honey Bees), Artificial Bee Colony algorithm (the process of recollection of Honey Bees); and (c) Meta-heuristics based on biological aspects of alive beings: Tabu Search Algorithm (Classical Conditioning on alive beings), Simulated Annealing algorithm (temperature control of spiders), Particle Swarm Optimization algorithm (social behavior and movement dynamics of birds and fish) and Artificial Immune System (immunological mechanism of the vertebrates).
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References
Abbass HA (2001) MBO: marriage in honey bees optimization-a haplometrosis polygynous swarming approach. Proc Congr Evolut Comput 1: 207–214. doi:10.1109/CEC.2001.934391
Barricelli NA (1954) Esempi numerici di processi di evoluzione. Methodos 6(21–22): 45–68
Boettcher S, Percus AG (1999) Extremal optimization: methods derived from co-evolution. Proc Genet Evolut Comput Conf 1: 825–832
Brownlee J (2007) Optimization algorithm toolkit, http://optalgtoolkit.sourceforge.net. Accessed 29 January 2010
Černý V (1985) A thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. J Optim Theory Appl 45: 41–51. doi:10.1007/BF00940812
Cirasella J, Johnson DS, McGeoch LA, Zhang W (2001) The asymmetric traveling salesman problem: algorithms, instance generators, and tests. In: Buchsbaum AL, Snoeyink J (eds) Third international workshop. ALENEX 2001, Springer, New York. Lecture notes in computer science vol 2153, pp 32–59
Cutello V, Narzisi G, Nicosia G, Pavone M (2005) Clonal selection algorithms: a comparative case study using effective mutation potentials, optIA versus CLONALG. In: 4th international conference on artificial immune systems-ICARIS 2005, Banff, Canada. Springer, New York. LNCS vol 3627, pp 13–28, 14–17
Díaz-Parra O, Cruz-Chávez MA (2008) Evolutionary algorithm with intelligent mutation operator that solves the vehicle routing problem of clustered classification with time windows. Polish J Environ Stud 17(4C), Hard pp 91–95
Dorigo M (1992) Optimization, learning and natural algorithms, Ph.D. thesis, Politecnico di Milano, Italy
Dorigo M (2009) Metaheuristics network website 2000. http://www.metaheuristics.net/. Visited in January
Dorigo M, Di Caro G, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5(2): 137–172. doi:10.1162/106454699568728
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micromachine and human science, Nagoya, Japan pp 39–43
Farmer JD, Packard N, Perelson A (1986) The immune system, adaptation and machine learning. Phys D 22(1-3): 187–204. doi:10.1016/0167-2789(81)90072-5
Feo TA, Resende MGC (1995) Greedy randomized adaptive search procedure. J Global Optim 6(2): 109–133. doi:10.1007/BF01096763
Fogel L, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New York
Gambardella LM, Dorigo M (1996) Solving symmetric and asymmetric TSPs by ant colonies. In: Proceedings of IEEE international conference on evolutionary computation pp 622–627
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2): 60–68. doi:10.1177/003754970107600201
Gladyshev EA, Meselson M, Arkhipova IR (2008) Massive horizontal gene transfer in bdelloid rotifers. Science 320: 1210–1213
Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13(5): 533–549. doi:10.1016/0305-0548(86)90048-1
Glover F (1989) Tabu search: part I. ORSA J Comput 1: 190–206
Glover F (1990) Tabu search: a tutorial. Interfaces 20(4): 74–94
Hansen P, Mladenovi N (2001) Variable neighborhood search: principles and applications. Euro J Oper Res 130(3): 449–467. doi:10.1016/S0377-2217(00)00100-4
Hastings WK (1970) Monte Carlo sampling methods using markov chains and their applications. Biometrika 57(1): 97–109
Helfman G, Collette B, Facey D (1997) The diversity of fishes. Blackwell Publishing, Oxford
Hess WH (1920) Notes on the biology of some common Lampyridae. Biol Bull 38(2): 39–76
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Piscataway, NJ, pp 1942–1948. doi:10.1109/ICNN.1995.488968
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science, New Series 220(4598):671-680. Stable http://www.jstor.org/stable/1690046
Koza JR (1990) Non-linear genetic algorithms for solving problems. United States Patent 4,935,877. Filed May 20, 1988. Issued June 19
Lederberg J, Tatum EL (1946) Novel genotypes in mixed cultures of biochemical mutants of bacteria. Cold Spring Harbor Symp Quant Biol 11:113–114
Lucic P (2002) Modelling transportation problems using concepts of swarm intelligence and soft computing, Ph.D. thesis, Faculty of the Virginia Polytechnic Institute and State University, Virginia
Margullis L (1967) On the origin of mitosing Cells. J Theor Bio 14(3): 255–274
Margullis L (1970) Origin of eukaryotic cells. Yale University Press, USA
Margulis L, Sagan D (1995) What is life? Simon & Schuster. New York
Martello S, Toth P (1991) Knapsack problems: algorithms and computer implementations. Wiley, England, pp 221–239
Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21: 1087–1092. doi:10.1063/1.1699114
Mitchel M (1999) An introduction to genetic algorithms. MIT Press, Cambridge
Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24(11): 1097–1100. doi:10.1016/S0305-0548(97)00031-2
Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms, Caltech concurrent computation program, C3P Report 826
Moyson F, Manderick B (1988) The collective behaviour of ants: an example of self-organization in massive parallelism. In: Actes de AAAI spring symposium on parallel models of intelligence, Stanford, Californie
Nakrani S, Tovey S (2004) On honey bees and dynamic server allocation in internet hosting centers. Adapt Behav 12(1–3): 223–240. doi:10.1177/105971230401200308
Or I (1976) Traveling salesman type combinatorial problems y their relations to the logistics of blood banking. Ph.D. Thesis. Department of Industrial Engineering y Management Sciences, Northwestern Univ
Park JM, Deem MW (2007) Phase diagrams of quasispecies theory with recombination and horizontal gene transfer. Phys Rev Lett 98(5): 1–4. doi:10.1103/PhysRevLett.98.058101
Pavlov I (1972) Conditioned reflexes: an investigation of the physiological activity of the cerebral cortex. Oxford University Press, Oxford
Rechenberg I (1965) Cybernetic solution path of an experimental problem. Royal Aircraft Establishment Library Translation, Farnborough
Reynolds CW (1987) Flocks, herds, and schools: a distributed behavioral model. Comput Gr (ACM SIGGRAPH ‘87 Conf Proc) 21(4): 25–34. doi:10.1145/37401.37406
Robbins H, Monro S (1951) A stochastic approximation method. Ann Math Stat 22: 400–407
Rubinstein RY (1997) Optimization of computer simulation models with rare events. Euro J Oper Res 99: 89–112. doi:10.1016/S0377-2217(96)00385-2
Ruiz-Vanoye JA (2010) REPOsitory of Combinatorial Optimization Problems & meta-heuristics (REPOCOP), http://repocop.ruizvanoye.com. Accessed 29 January 2010
Ruiz-Vanoye JA, Díaz-Parra O (2010) Transgenic algorithm for the solution of the cargo management and scheduling of transport vehicles. Unpublished manuscript
Smith SF (1980) A learning system based on genetic adaptive algorithms, Ph.D. dissertation in University of Pittsburgh
Srinivas M, Patnaik LM (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4): 656–667
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4): 341–359. doi:10.1023/A:1008202821328
Squire LR, Stark CE, Clark RE (2004) The medial temporal lobe. Annu Rev Neurosci 27: 279–306
Wen-liang Z, Jun Z, Wei-neng C (2007) A novel discrete particle swarm optimization to solve traveling salesman problem. In: IEEE congress on evolutionary computation, CEC, pp 3283–3287. doi:10.1109/CEC.2007.4424894
Wright S (1977) Evolution and genetics of populations, vol. 3. Experimental results and evolutionary deductions. University of Chicago Press, Chicago
Yang XS (2008) Firefly algorithm (chapter 8), Nature-inspired metaheuristic algorithms. Luniver Press, Beckington
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Ruiz-Vanoye, J.A., Díaz-Parra, O. Similarities between meta-heuristics algorithms and the science of life. Cent Eur J Oper Res 19, 445–466 (2011). https://doi.org/10.1007/s10100-010-0135-x
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DOI: https://doi.org/10.1007/s10100-010-0135-x