Evolutionary self-adaptation: A survey of operators and strategy parameters

Evolutionary Intelligence

Volumn 3, Issue 2, 2010, Pages 51-65

  Kramer, Oliver ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Covariance matrix self adaptation; Crossover; Evolution strategies; Mutation; Parameter control; Self adaptation

Indexed keywords

EVOLUTION STRATEGIES; EVOLUTIONARY COMPUTATIONS; EVOLUTIONARY OPERATORS; EVOLUTIONARY SEARCH; GENETIC OPERATORS; ILL-CONDITIONED; ON-LINE PARAMETER; OPTIMIZATION PROCESS; PARAMETER CONTROL; PARAMETER SETTING; SEARCH TECHNIQUE; SELF ADAPTATION; SELF ADAPTIVE CONTROL; SELF-ADAPTIVE;

BIOLOGY; COVARIANCE MATRIX; HEURISTIC METHODS; OPTIMIZATION; SURVEYS;

EVOLUTIONARY ALGORITHMS;

EID: 77955090318     PISSN: 18645909     EISSN: 18645917     Source Type: Journal    
DOI: 10.1007/s12065-010-0035-y     Document Type: Article

References (76)

1. Angeline PJ (1995) Adaptive and self-adaptive evolutionary computations. In: Palaniswami M, Attikiouzel Y (eds) Computational intelligence a dynamic systems perspective. IEEE Press, New York, pp 152-163.
2. Arnold DV, Brauer D (2008) On the behaviour of the (1+1)-ES for a simple constrained problem. In: Proceedings of the 10th conference on parallel problem solving from nature-PPSN X, pp 1-10.
3. Auger A (2003) Convergence results for (1, λ)-SA-ES using the theory of φ{symbol}-irreducible markov chains. In: Proceedings of the evolutionary algorithms workshop of the 30th international colloquium on automata, languages and programming.
4. Bäck T (1998) An overview of parameter control methods by self-adaption in evolutionary algorithms. Fundam Inf 35(1-4): 51-66.
5. Bartz-Beielstein T (2006) Experimental research in evolutionary computation: the new experimentalism. Natural computing series. Springer, April.
6. Bartz-Beielstein T, Lasarczyk C, Preu M (2005) Sequential parameter optimization. In: McKay B, et al (eds) Proceedings of the IEEE congress on evolutionary computation-CEC, vol 1. IEEE Press, pp 773-780.
7. Bartz-Beielstein T, Preuss M (2006) Considerations of budget allocation for sequential parameter optimization (SPO). In: Paquete L, et al. (eds) Workshop on empirical methods for the analysis of algorithms, proceedings, Reykjavik, Iceland, pp 35-40.
8. Bäck T (1991) Self-adaptation in genetic algorithms. In: Proceedings of the 1st European conference on artificial life-ECAL, pp 263-271.
9. Bäck T (1992) The interaction of mutation rate, selection, and self-adaptation within a genetic algorithm. In: Proceedings of the 2nd conference on parallel problem solving from nature-PPSN II, pp 85-94.
10. Bäck T, Schütz M (1996) Intelligent mutation rate control in canonical genetic algorithms. In: Foundation of intelligent systems, 9th international symposium, ISMIS '96. Springer, pp 158-167.
11. Berlik S (2004) A step size preserving directed mutation operator. In: Proceedings of the 6th conference on genetic and evolutionary computation-GECCO, pp 786-787.
12. Beyer H-G (2001) The theory of evolution strategies. Springer, Berlin.
13. Beyer H-G, Deb K (2001) On self-adaptive features in real-parameter evolutionary algorithms. IEEE Trans Evol Comput 5(3): 250-270.
14. Beyer H-G, Meyer-Nieberg S (2006) Self-adaptation on the ridge function class: first results for the sharp ridge. In: Proceedings of the 9th conference on parallel problem solving from nature-PPSN IX, pp 72-81.
15. Beyer HG, Schwefel HP (2002) Evolution strategies-a comprehensive introduction. Nat Comput 1: 3-52.
16. Beyer HG, Sendhoff B (2008) Covariance matrix adaptation revisited-the cmsa evolution strategy. In: Proceedings of the 10th conference on parallel problem solving from nature-PPSN X, pp 123-132.
17. Coello CA (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2): 113-127.
18. Davis L (1989) Adapting operator probabilities in genetic algorithms. In: Proceedings of the 3rd international conference on genetic algorithms, San Francisco, Morgan Kaufmann Publishers Inc, pp 61-69.
19. de Landgraaf W, Eiben A, Nannen V (2007) Parameter calibration using meta-algorithms. In: Proceedings of the IEEE congress on evolutionary computation-CEC, pp 71-78.
20. DeJong K (2007) Parameter setting in EAs: a 30 year perspective. In: Parameter setting in evolutionary algorithms, studies in computational intelligence. Springer, pp 1-18.
21. Eiben A, Schut MC, de Wilde A (2006) Is self-adaptation of selection pressure and population size possible? A case study. In: Proceedings of the 9th conference on parallel problem solving from nature-PPSN IX, pp 900-909.
22. Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2): 124-141.
23. Eiben AE, Michalewicz Z, Schoenauer M, Smith JE (2007) Parameter control in evolutionary algorithms. In: Parameter setting in evolutionary algorithms, studies in computational intelligence. Springer, pp 19-46.
24. Fogarty TC (1989) Varying the probability of mutation in the genetic algorithm. In: Proceedings of the 3rd international conference on genetic algorithms, San Francisco, Morgan Kaufmann Publishers Inc, pp 104-109.
25. Fogel DB, Fogel LJ, Atma JW (1991) Meta-evolutionary programming. In: Proceedings of 25th asilomar conference on signals, systems & computers, pp 540-545.
26. georg Beyer H, Arnold DV (2003) Qualms regarding the optimality of cumulative path length control in csa/cma-evolution strategies. Evol Comput 11.
27. Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, Reading.
28. Grefenstette J (1986) Optimization of control parameters for genetic algorithms. IEEE Trans Syst Man Cybern 16(1): 122-128.
29. Hansen N (2006) An analysis of mutative sigma self-adaptation on linear fitness functions. Evol Comput 14(3): 255-275.
30. Harik GR, Goldberg DE (1997) Learning linkage. In: Foundations of genetic algorithms 4. Morgan Kaufmann, pp 247-262.
31. Harik GR, Lobo FG, Sastry K (2006) Linkage learning via probabilistic modeling in the extended compact genetic algorithm (ECGA). In: Scalable optimization via probabilistic modeling, studies in computational intelligence, Springer, pp 39-61.
32. Herdy M (1992) Reproductive isolation as strategy parameter in hierarchically organized evolution strategies. In: Proceedings of the 10th conference on parallel problem solving from nature-PPSN II, pp 207-217.
33. Hesser J, Männer R (1990) Towards an optimal mutation probability for genetic algorithms. In: Proceedings of the 10th conference on parallel problem solving from nature-PPSN I, London, UK, Springer-Verlag, pp 23-32.
34. Hesser J, Männer R (1992) Investigation of the m-heuristic for optimal mutation probabilities. In PPSN, pp 115-124.
35. Hildebrand L (2002) Asymmetrische evolutionsstrategien. PhD thesis, University of Dortmund.
36. Holland JH (1992) Adaptation in natural and artificial systems, 1st edn, MIT Press, Cambridge.
37. Jägersknpper J (2005) Rigorous runtime analysis of the (1+1) es: 1/5-rule and ellipsoidal fitness landscapes. In: Proceedings of the workshop on foundation of genetic algorithms FOGA, pp 260-281.
38. Jägersknpper J (2006) Probabilistic runtime analysis of (1 + λ)es using isotropic mutations. In: Proceedings of the 8th conference on genetic and evolutionary computation-GECCO, New York, ACM, pp 461-468.
39. Joines J, Houck C (1994) On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GAs. In: Fogel DB (eds) Proceedings of the 1st IEEE conference on evolutionary computation, Orlando, Florida, IEEE Press, pp 579-584.
40. Jong KAD (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan.
41. Koumoutsakos P, Muller SD (2006) Flow optimization using stochastic algorithms. Lecture Notes Control Inf Sci 330: 213-229.
42. Kramer O (2008) Premature convergence in constrained continuous search spaces. In: Proceedings of the 10th conference on parallel problem solving from nature-PPSN X, Berlin, Springer, to appear.
43. Kramer O (2008) Self-adaptive inversion mutation for combinatorial representations. In: Proceedings of the 2008 international conference on genetic and evolutionary methods, to appear.
44. Kramer O, Barthelmes A, Rudolph G (2009) Surrogate constraint functions for cma evolution strategies. In: Proceedings of the conference on artificial intelligence and automation, page to appear.
45. Kramer O, Koch P (2007) Self-adaptive partially mapped crossover. In: Proceedings of the 9th conference on genetic and evolutionary computation-GECCO, New York, ACM Press, pp 1523-1523.
46. Kramer O, Ting CK, Büning HK (2005) A new mutation operator for evolution strategies for constrained problems. In: Proceedings of the IEEE congress on evolutionary computation-CEC, pp 2600-2606.
47. Kursawe F (1999) Grundlegende empirische Untersuchungen der Parameter von Evolutionsstrategien-Metastrategien. PhD thesis, University of Dortmund.
48. Liang KH, Yao X, Liu Y, Newton CS, Hoffman D (1998) An experimental investigation of self-adaptation in evolutionary programming. In: Proceedings of the 7th international conference on evolutionary programming VII-EP, Berlin, Springer, pp 291-300.
49. Maruo MH, Lopes HS, Delgado MR (2005) Self-adapting evolutionary parameters: encoding aspects for combinatorial optimization problems. In: Proceedings of EvoCOP, pp 154-165.
50. Mercer RE, Sampson JR (1978) Adaptive search using a reproductive metaplan. Kybernetes 7: 215-228.
51. Mersch B, Glasmachers T, Meinicke P, Igel C (2006) Evolutionary optimization of sequence kernels for detection of bacterial gene starts. In: ICANN (2), pp 827-836.
52. Meyer-Nieberg S, Beyer HG (2007) Self-adaptation in evolutionary algorithms. In: Lobo FG, Lima CF, Michalewicz Z (eds) Parameter setting in evolutionary algorithms. Springer, Berlin.
53. Mezura-Montes E, Palomeque-Ortiz AG (2009) Self-adaptive and deterministic parameter control in differential evolution for constrained optimization. Constraint-Handl Evol Optim 189: 95-120.
54. Mühlenbein H (1992) How genetic algorithms really work: mutation and hillclimbing. In: Proceedings of the 2nd conference on parallel problem solving from nature-PPSN II, pp 15-26.
55. Nannen V, Eiben A (2006) A method for parameter calibration and relevance estimation in evolutionary algorithms. In: Proceedings of the 8th conference on genetic and evolutionary computation-GECCO, New York, ACM Press, pp 183-190.
56. Nannen V, Eiben A (2007) Relevance estimation and value calibration of evolutionary algorithm parameters. In: IJCAI, pp 975-980.
57. Ostermeier A, Gawelczyk A, Hansen N (1994) A derandomized approach to self adaptation of evolution strategies. Evol Comput 2(4): 369-380.
58. Ostermeier A, Gawelczyk A, Hansen N (1995) A derandomized approach to self adaptation of evolution strategies. Evol Comput 2(4): 369-380.
59. Preuss M, Bartz-Beielstein T (2007) Sequential parameter optimization applied to self-adaptation for binary-coded evolutionary algorithms. In: Parameter setting in evolutionary algorithms, studies in computational intelligence. Springer, pp 91-119.
60. Rechenberg I (1973) Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, Stuttgart.
61. Rechenberg I (1994) Evolutionsstrategie '94. Frommann-Holzboog, Stuttgart.
62. Reed J, Toombs R, Barricelli NA (1967) Simulation of biological evolution and machine learning: I. selection of self-reproducing numeric patterns by data processing machines, effects of hereditary control, mutation type and crossing. J Theor Biol 17: 319-342.
63. Reinelt G (1991) Tsplib-a traveling salesman problem library. ORSA J Comput 3: 376-384.
64. Rosenberg RS (1967) Simulation of genetic populations with biochemical properties. PhD thesis, University of Michigan.
65. Rudolph G (2001) Self-adaptive mutations may lead to premature convergence. IEEE Trans Evol Comput 5(4): 410-414.
66. Schaffer JD, Caruana R, Eshelman LJ, Das R (1989) A study of control parameters affecting online performance of genetic algorithms for function optimization. In: Proceedings of the 3rd international conference on genetic algorithms-ICGA 1989, pp 51-60.
67. Schaffer JD, Morishima A (1987) An adaptive crossover distribution mechanism for genetic algorithms. In: Proceedings of the second international conference on genetic algorithms on genetic algorithms and their application, Hillsdale, NJ, USA, L. Erlbaum Associates Inc, pp 36-40.
68. Schwefel H-P (1974) Adaptive Mechanismen in der biologischen Evolution und ihr Einflus auf die Evolutionsgeschwindigkeit. Interner Bericht der Arbeitsgruppe Bionik und Evolutionstechnik am Institut fnr Mess- und Regelungstechnik, TU Berlin.
69. Schwefel HP (1995) Evolution and Optimum Seeking. Sixth-generation computer technology. Wiley Interscience, New York.
70. Semenov MA, Terkel DA (2003) Analysis of convergence of an evolutionary algorithm with self-adaptation using a stochastic lyapunov function. Evol Comput 11(4): 363-379.
71. Smith J (2001) Modelling GAs with self adaptive mutation rates. In: Proceedings of the genetic and evolutionary computation conference, pp 599-606.
72. Smith J, Fogarty TC (1996) Recombination strategy adaptation via evolution of gene linkage. In: Proceedings of the IEEE congress on evolutionary computation-CEC, pp 826-831.
73. Smith J, Fogarty TC (1996) Self adaptation of mutation rates in a steady state genetic algorithm. In: Proceedings of the international conference on evolutionary computation-ICEC, pp 318-323.
74. Spears WM (1995) Adapting crossover in evolutionary algorithms. In: McDonnell JR, Reynolds RG, Fogel DB (eds) Proceedings of the fourth annual conference on evolutionary programming, Cambridge, MIT Press, pp 367-384.
75. Stone C, Smith J (2002) Strategy parameter variety in self-adaptation of mutation rates. In: Proceedings of the genetic and evolutionary computation conference-GECCO, San Francisco, Morgan Kaufmann Publishers Inc, pp 586-593.
76. Weinberg R (1970) Computer simulation of a living cell. PhD thesis, University of Michigan.