Abstract
In our description of evolutionary algorithms we make use of terms that stem from biology, hinting at the roots of evolutionary algorithms. We adhere to these standard notions as long as they do not collide with standard notions in computer science. Evolutionary algorithms are structurally very simple. They work in rounds that are called generations. Evolutionary algorithms operate on some search spaceS, where S is a set. Points are assigned some quality via a function f.
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References
E. Aarts, J. Korst, Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing (Wiley, New York, 1998)
E. Aarts, J.K. Lenstra (eds.), Local Search in Combinatorial Optimization (Princeton University Press, Princeton, 2003)
T. Bäck, An overview of parameter control methods by self-adaptation in evolutionary algorithms. Fundam. Inform. 35(1–4), 51–66 (1998)
T. Bäck, D.B. Fogel, Z. Michalewicz (eds.), Handbook of Evolutionary Computation (IOP Publishing/Oxford University Press, Bristol/Oxford, 1997)
T. Bartz-Beielstein, Experimental Research in Evolutionary Computation: The New Experimentalism (Springer, Berlin, 2006)
T.H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein, Introduction to Algorithms, 2nd edn. (MIT, Cambridge, 2001)
S. Droste, D. Wiesmann, On the design of problem-specific evolutionary algorithms, in Advances in Evolutionary Computing, ed. by A. Ghosh, S. Tsutsui (Springer, Berlin, 2003), pp. 153–173
A. Eiben, R. Hinterding, Z. Michalewicz, Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput. 3(2), 124–141 (1999)
L.J. Fogel, A.J. Owens, M.J. Walsh, Artificial Intelligence Through Simulated Evolution (Wiley, New York, 1966)
D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading, 1989)
J. Holland, Adaptation in Natural and Artificial Systems (University of Michigan Press, Ann Arbor, 1975)
T. Jansen, Simulated annealing, in Theory of Randomized Search Heuristics, ed. by A. Auger, B. Doerr (World Scientific, Singapore, 2011), pp. 171–196
T. Jansen, I. Wegener, A comparison of simulated annealing with simple evolutionary algorithms on pseudo-Boolean functions of unitation. Theor. Comput. Sci. 386, 73–93 (2007)
S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT, Cambridge, 1992)
J.R. Koza, Genetic Programming II: Automatic Discovery of Resuable Programs (MIT, Cambridge, 1994)
J.R. Koza, F.H. Bennett III, D. Andre, M.A. Keane, Genetic Programming III: Darwinian Invention and Problem Solving (Morgan Kaufmann, San Francisco, 1999)
J.R. Koza, M.A. Keane, M.J. Streeter, W. Mydlowec, J. Yu, G. Lanza, Genetic Programming IV: Routine Human-Competitive Machine Intelligence (Kluwer Academic, New York, 2003)
N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, E. Teller, Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092 (1953)
W. Michiels, E. Aarts, J. Korst, Theoretical Aspects of Local Search (Springer, Berlin, 2007)
R. Motwani, P. Raghavan, Randomized Algorithms (Cambridge University Press, Cambridge, 1995)
R. Poli, Tournament selection, iterated coupon-collection problem, and backward-chaining evolutionary algorithms, in Foundations of Genetic Algorithms (FOGA 2005), Aizu-Wakamatsu, ed. by A.H. Wright, M.D. Vose, K.A. De Jong, L.M. Schmitt. Lecture Notes in Computer Science, vol. 3469 (Springer, Berlin, 2005), pp. 132–155
I. Rechenberg, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der Evolution (Frommann-Holzboog, Stuttgart-Bad Cannstatt, 1973)
G. Rozenberg, T. Bäck, J.N. Kok (eds.), Handbook of Natural Computing (Springer, Berlin, 2012)
H.-P. Schwefel, Evolution and Optimum Seeking (Wiley, New York, 1995)
M.D. Vose, The Simple Genetic Algorithm: Foundations and Theory (MIT, Cambridge, 1999)
I. Wegener, J. Scharnow, K. Tinnefeld, The analysis of evolutionary algorithms on sorting and shortest paths problems. J. Math. Model. Algorithms 3, 349–366 (2004)
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Jansen, T. (2013). Evolutionary Algorithms and Other Randomized Search Heuristics. In: Analyzing Evolutionary Algorithms. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17339-4_2
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DOI: https://doi.org/10.1007/978-3-642-17339-4_2
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