Coverage for moptipy / algorithms / modules / selections / best.py: 100%

1""" 

2Choose the `n` best elements from the source array. 

3 

4"Best" selection is the standard way to perform survival selection in a 

5(mu+lambda) Evolutionary Algorithm (:class:`~moptipy.algorithms.so.ea.EA`). 

6It basically sorts the input array based on fitness and then copies the first 

7`n` elements to the output array. The algorithm is not random but 

8deterministic, i.e., it will always select each of the `n` best records 

9exactly once. 

10 

11This selection is also often called "mu+lambda," "mu,lambda," or "top-n" 

12selection. It is similar to the selection scheme in Eshelman's CHC algorithm. 

13A randomized version of this algorithm is called "truncation selection" in the 

14paper of Blickle and Thiele. 

15 

161. Peter J. B. Hancock. An Empirical Comparison of Selection Methods in 

17 Evolutionary Algorithms. In Terence Claus Fogarty, editor, *Selected Papers 

18 from the AISB Workshop on Evolutionary Computing (AISB EC'94),* April 

19 11-13, 1994, Leeds, UK, volume 865 of Lecture Notes in Computer Science, 

20 pages 80-94, Berlin/Heidelberg, Germany: Springer, ISBN: 978-3-540-58483-4. 

21 https://dx.doi.org/10.1007/3-540-58483-8_7. Conference organized by the 

22 Society for the Study of Artificial Intelligence and Simulation of 

23 Behaviour (AISB). 

242. Larry J.Eshelman. The CHC Adaptive Search Algorithm: How to Have Safe 

25 Search When Engaging in Nontraditional Genetic Recombination. In Gregory 

26 J. E. Rawlins, editor, Foundations of Genetic Algorithms, volume 1, 1991, 

27 pages 265-283. San Francisco, CA, USA: Morgan Kaufmann. 

28 https://doi.org/10.1016/B978-0-08-050684-5.50020-3 

293. Frank Hoffmeister and Thomas Bäck. Genetic Algorithms and Evolution 

30 Strategies: Similarities and Differences. In Hans-Paul Schwefel and 

31 Reinhard Männer, *Proceedings of the International Conference on Parallel 

32 Problem Solving from Nature (PPSN I),* October 1-3, 1990, Dortmund, 

33 Germany, volume 496 of Lecture Notes in Computer Science, pages 455-469, 

34 Berlin/Heidelberg, Germany: Springer. ISBN: 978-3-540-54148-6. 

35 https://doi.org/10.1007/BFb0029787. 

364. Uday Kumar Chakraborty and Kalyanmoy Deb and Mandira Chakraborty. Analysis 

37 of Selection Algorithms: A Markov Chain Approach. *Evolutionary 

38 Computation,* 4(2):133-167. Summer 1996. Cambridge, MA, USA: MIT Press. 

39 doi:10.1162/evco.1996.4.2.133. 

40 https://dl.acm.org/doi/pdf/10.1162/evco.1996.4.2.133 

415. Tobias Blickle and Lothar Thiele. A Comparison of Selection Schemes used in 

42 Genetic Algorithms. Second edition, December 1995. TIK-Report 11 from the 

43 Eidgenössische Technische Hochschule (ETH) Zürich, Department of Electrical 

44 Engineering, Computer Engineering and Networks Laboratory (TIK), Zürich, 

45 Switzerland. ftp://ftp.tik.ee.ethz.ch/pub/publications/TIK-Report11.ps 

46""" 

47 

48from typing import Any, Callable 

49 

50from numpy.random import Generator 

51 

52from moptipy.algorithms.modules.selection import FitnessRecord, Selection 

53 

54 

55# start book 

56class Best(Selection): 

57 """The best selection: select each of the best `n` elements once.""" 

58 

59 def select(self, source: list[FitnessRecord], 

60 dest: Callable[[FitnessRecord], Any], 

61 n: int, random: Generator) -> None: # pylint: disable=W0613 

62 """ 

63 Perform deterministic best selection without replacement. 

64 

65 :param source: the list with the records to select from 

66 :param dest: the destination collector to invoke for each selected 

67 record 

68 :param n: the number of records to select 

69 :param random: the random number generator 

70 """ 

71 source.sort() # sort by fitness, best solutions come first 

72 for i in range(n): # select the n first=best solutions 

73 dest(source[i]) # by sending them to dest 

74# end book 

75 

76 def __str__(self): 

77 """ 

78 Get the name of the best selection algorithm. 

79 

80 :return: the name of the best selection algorithm 

81 """ 

82 return "best"