Evolutionary computation

Nature Reviews Genetics

Volumn 2, Issue 6, 2001, Pages 428-436

  Foster, James A ^a  

^a University of Idaho   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DNA;

ALGORITHM; BIOLOGY; EVOLUTION; REVIEW;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DNA; EVOLUTION;

EID: 0035375033     PISSN: 14710056     EISSN: None     Source Type: Journal    
DOI: 10.1038/35076523     Document Type: Review

References (75)

1. Danielson, W. F., Foster, J. A. & Frincke, D. in Proc. Int. Conf. Evolutionary Computing (eds Fogel, D. B. & Angeline, P. J.) 259-264 (IEEE Press, Piscataway, New Jersey, 1998).
2. Shoaf, J. S. & Foster, J. A. in Proc. 1996 Annu. Meeting of the Decision Sciences Institute Vol. 2, 571-573 (Decision Sciences Institute, Orlando, Florida, 1996).
3. Marconi, J. & Foster, J. A. in Proc. Int. Conf. Evolutionary Computing (eds Fogel, D. B. & Angeline, P. J.) 650-655 (IEEE Press, Piscataway, New Jersey, 1998).
4. Notredame, C. & Higgins, D. G. SAGA: sequence alignment by genetic algorithm. Nucleic Acids Res. 24, 1515-1524 (1996).
5. Chellapilla, K. & Fogel, G. B. in Proc. Congr. Evolutionary Computation (eds Angeline, P. J. & Porto, V. W.) 445-452 (IEEE Press, Piscataway, New Jersey, 1999).
6. Zhang, C. & Wong, A. K. C. Toward efficient multiple molecular sequence alignment: a system of genetic algorithm and dynamic programming. IEEE Trans. Syst. Man Cybernet. B: Cybernet. 27, 918-932 (1997).
7. Lewis, P. O. A genetic algorithm for maximum-likelihood phylogeny inference using nucleotide sequence data. Mol. Biol. Evol. 15, 277-283 (1998).
8. Koza, J. R., Mydlowec, W., Lanza, G., Yu, J. & Keane, M. A. in Pacific Symp. Biocomputing 2001 (eds Altman, R. B., Dunker, A. K., Hunker, L., Lauderdale, K. & Klein, T. E.) 434-445 (World Scientific, Singapore, 2001).
9. Goh, G. K.-M. & Foster, J. A. in Proc. Genetic and Evolutionary Computation Conf. (GECCO-2000) (eds Whitley, D. et al.) 27-33 (Morgan Kaufmann, San Francisco, California, 2000).
10. Gehlhaar, D. et al. Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem. Biol. 2, 317-324 (1995).
11. Back, T. Evolutionary Algorithms in Theory and Practice: Evolutionary Strategies, Evolutionary Programming. Genetic Algorithms (Oxford Univ. Press, New York, 1996). Survey of all evolutionary computation types, with mathematical characterization of their properties.
12. Holland, J. H. Adaptation in Natural and Artificial Systems (MIT Press, Cambridge, Massachusetts, 1975). Readable introduction to genetic algorithms, with interesting additional comments on artificial ecosystems.
13. Fraser, A. S. Simulation of genetic systems by automatic digital computers. I. Introduction. Aust. J. Biol. Sci. 10, 484-491 (1957).
14. Krasnogor, N., Hart, W. E., Smith, J. & Pelta, D. A. in Proc. Genetic and Evolutionary Computation Conf. (eds Banzhaf, W. et al.) 1596-1601 (Morgan Kaufmann, San Francisco, California, 1999).
15. Blickle, T. in Handbook of Evolutionary Computation (eds Bäck, T., Fogel, D. B. & Michalewicz, Z.) C2.3:1-4 (Oxford Univ. Press, New York, 1997).
16. Fogel, D. B. Evolutionary Computation: Towards a New Philosophy of Machine Intelligence (IEEE Press, Piscataway, New Jersey, 1999).
17. Fogel, D. B. in Proc. 2nd Annu. Conf. Evolutionary Programming (eds Fogel, D. B. & Atmar, W.) 23-29 (IEEE Press, Piscataway, New Jersey, 1993).
18. Fogel, L. J., Owens, A. & Walsh, M. Artificial Intelligence Through Simulated Evolution (John Wiley & Sons, New York, 1966).
19. Bäck, T., Hammel, U. & Schwefel, H.-P. Evolutionary computation: comments on the history and current state. IEEE Trans. Evol. Comput. 1, 3-17 (1997). Thorough presentation of main types of evolutionary computation (EC), with a complete discussion of EC history.
20. Reed, J., Toombs, R. & Barricelli, N. A. Simulation of biological evolution and machine learning. I. Selection of self-reproducing numeric patterns by data processing machines, effects of hereditary control, mutation and type crossing. J. Theor. Biol. 17, 319-342 (1967).
21. Rosenberg, R. Simulation of Genetic Populations with Biochemical Properties (Univ. of Michigan, Ann Arbor, 1967).
22. Fogel, D. B., Fogel, L. J. & Atmar, J. W. in Proc. 25th Asilomar Conf. Signals, Systems and Computers (ed. Chen, R. R.) 540-545 (Pacific Grove, California, 1991).
23. Koza, J. R. Genetic Programming. II. Automatic Discovery of Reusable Programs (MIT Press, Cambridge, Massachusetts, 1994).
24. Koza, J. R. Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, Massachusetts, 1992).
25. Mayley, G. Landscapes, learning costs and genetic assimilation. Evol. Comput. 4, 213-234 (1996).
26. Smith, R. E. in Handbook of Evolutionary Computation (eds Bäck, T., Fogel, D. B. & Michalewicz, Z.) B1. 5:6-11 (Oxford Univ. Press, New York, 1997).
27. Holland, J. H. Adaptation in Natural and Artificial Systems (MIT Press, Cambridge, Massachusetts, 1992).
28. Russo, M. Genetic fuzzy learning. IEEE Trans. Evol. Comput. 4, 259-273 (2000).
29. Banzhaf, W., Nordin, P., Keller, R. E. & Francone, F. D. Genetic Programming - An Introduction; On the Automatic Evolution of Computer Programs and its Applications (Morgan Kaufmann, San Francisco, California, 1998). Textbook on genetic programming, with a survey of many applications.
30. Foster, J. A. Discipulus: the first commercial genetic programming system. J. Genetic Programming Evolvable Hardware 2, 201-203 (2001).
31. Ryan, C., Collins, J. J. & O'Neill, M. in Proc. 1st European Workshop on Genetic Programming (eds Banzhaf, W., Poli, R., Schoenauer, M. & Fogarty, T. C.) 83-95 (Springer, New York, 1998).
32. Koza, J. R., Bennett, F. H., Andre, D. & Keane, M. A. Genetic Programming. III. Darwinian Invention and Problem Solving (Morgan Kaufmann, San Francisco, California, 1999).
33. Koza, J. R., Bennett, F. H., Andre, D., Keane, M. A. & Dunlap, F. Automated synthesis of analog electrical circuits by means of genetic programming. IEEE Trans. Evol. Comput. 1, 109-128 (1997).
34. Miller, J. F., Job, D. & Vassilev, V. K. Principles in the evolutionary design of digital circuits. Part I. Genetic Programming Evolvable Machines 1, 7-35 (2000).
35. Miller, J. F., Job. D. & Vassilev, V. K. Principles in the evolutionary design of digital circuits. Part II. Genetic Programming Evolvable Machines 1, 259-288 (2000).
36. Miller, J. F. in Proc. Genetic and Evolutionary Computation Conf. (eds Banzhaf, W. et al.) 1135-1142 (Morgan Kaufmann, San Francisco, California, 1999).
37. Masner, J., Cavalieri, J., Frenzel, J. & Foster, J. A. in Proc. NASA/DoD Workshop on Evolvable Hardware (eds Stoica, A., Keymenlen, D. & John, J.) 255-261 (IEEE Press, Piscataway, New Jersey, 1999).
38. Thompson, A. in Genetic Programming 1996: Proc. 1st Annu. Conf. (eds Koza, J. R., Goldberg, D. E., Fogel, D. B. & Riolo, R. L.) 444-452 (Morgan Kaufmann, San Francisco, California, 1996).
39. Dumoulin, J., McGrew, S., Frenzel, J. & Foster, J. A. in Proc. Int. Workshop on Evolvable Image and Digital Signal Processing (ed. Cagnoni, S.) 1-11 (Springer, New York, 2000).
40. Thompson, A., Harvey, I. & Husbands, P. in Towards Evolvable Hardware; The Evolutionary Engineering Approach (eds Sanchez, E. & Tomassini, M.) 136-165 (Springer, Berlin, 1996).
41. Masner, J., Cavalieri, J., Frenzel, J. & Foster, J. A. in Proc. NASA/DoD Workshop on Evolvable Hardware (eds Stoica, A., Keymenlen, D. & John, J.) 81-90 (IEEE Press, Piscataway, New Jersey, 2000).
42. Barkai, N. & Leibler, S. Robustness in simple biochemical networks. Nature 387 (1997).
43. Wolpert, D. H. & Macready, W. G. No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67-82 (1997).
44. Culberson, J. C. On the futility of blind search: an algorithmic view of 'no free lunch'. Evol. Comput. 6, 109-127 (1998). Readable presentation of 'no free lunch' theorems, an important part of evolutionary computation theory.
45. Whitely, D. in Proc. Genetic and Evolutionary Computation Conf. (eds Banzhaf, W. et al.) 833-839 (Morgan Kaufmann, San Francisco, California, 1999).
46. Angeline, P. J. & Pollack, J. B. in Artficial Life III (ed. Langton, C.) 55-71 (Addison-Wesley Longman, Reading, Massachusetts, 1994).
47. Rosca, J. P. & Ballard, D. H. in Advances in Genetic Programming 2 (ed. Angeline, P. J. & Kinnear, K. E.) 177-202 (MIT Press, Cambridge, Massachusetts, 1996).
48. Jones, T. in Proc. 6th Int. Conf. Genetic Algorithms (ed. Eshelman, L. J.) 73-80 (Morgan Kaufmann, San Francisco, California, 1995).
49. Angeline, P. J. in Genetic Programming 1997: Proc. 2nd Annu. Conf. (eds Koza, J. R. et al.) 9-17 (Stanford Univ., California, 1997).
50. O'Reilly, U.-M. & Oppacher, F. in Parallel Problem Solving from Nature (PPSN III) (eds Davidor, Y., Schwefel, H.-P. & Männer, R.) 397-406 (Springer, New York, 1994).
51. Goldberg, D. Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading, Massachusetts, 1989).
52. Lipson, H. & Pollack, J. B. Automatic design and manufacture of artificial lifeforms. Nature 406, 974-978 (2000).
53. Whitley, D. L., Gordon, V. S. & Mathias, K. E. in Parallel Problem Solving from Nature (eds Davidor, Y., Schwefel, H.-P. & Männer, R.) 6-15 (Springer, Berlin, 1994).
54. Turney, P., Whitely, D. & Anderson, R. Evolution, learning, and instinct: 100 years of the Baldwin effect. Evol. Comput. 4, iv-viii (1996).
55. Hinton, G. E. & Nowlan, S. J. How learning can guide evolution. Complex Syst. 1, 495-502 (1987).
56. Schaffer, J. D. & Morishima, A. in Genetic Algorithms and their Applications: Proc. 2nd Int. Conf. Genetic Algorithms (ed. Grefenstette, J. J.) 36-40 (Morgan Kaufmann, San Francisco, California, 1987).
57. Louis, S. J. & Rawlins, G. J. E. in Proc. 4th Int. Conf. Genetic Algorithms (eds Belew, R. K. & Booker, L. B.) 53-60 (Morgan Kaufmann, San Mateo, California, 1991).
58. Hadad, B. S. & Eick, C. F. in Evolutionary Programming VI (eds Angeline, P. J., Reynolds, R. G., McDonnell, J. R. & Eberhart, R.) 223-234 (Springer, New York, 1997).
59. Francone, F. D., Conrads, M., Banzhaf, W. & Nordin, P. in Proc. Genetic and Evolutionary Computation Conf. (eds Banzhaf, W. et al.) 1021-1026 (Orlando, Florida, 1999).
60. Langdon, W. B. Size fair and homologous tree genetic programming crossovers. Genetic Programming Evolvable Machines 1, 95-119 (2000).
61. Langdon, W. B., Soule, T., Poli, R. & Foster, J. A. in Advances in Genetic Programming 3 (eds Spector, L, Langdon, W. B., O'Reilly, U.-M. & Angeline, P. J.) 163-190 (MIT Press, Cambridge, Massachusetts, 1999).
62. Soule, T. & Foster, J. A. Effects of code growth and parsimony pressure on populations in genetic programming. Evol. Comput 6, 293-309 (1998).
63. Soule, T., Foster, J. A. & Dickinson, J. in Genetic Programming 1996: Proc. 1st Annu. Conf. (eds Koza, J. R., Goldberg, D. E., Fogel, D. B. & Riolo, R. L.) 215-223 (Morgan Kaufmann, San Francisco, California, 1996).
64. Leung, K.-S., Duan, Q.-H., Xu, Z.-B. & Wong, C. K. A new model of simulated evolution computation - convergence analysis and specifications. IEEE Trans. Evol. Comput. 5, 3-16 (2001).
65. Deb, K. & Spears, W. M. in Handbook of Evolutionary Computation (eds Bäck, T., Fogel, D. B. & Michalewicz, Z.) C6. 2: 1-5 (Oxford Univ. Press, New York, 1997).
66. Paredis, J. in Evolutionary Computation. 2. Advanced Algorithms and Operators (eds Bäck, T., Fogel, D. B. & Michalewicz, Z.) 224-238 (Institute of Physics Publishers, Bristol, UK, 2000).
67. Dorigo, M. & Caro, G. D. in New Ideas in Optimization (eds Corne, D., Dorigo, M. & Glover, F.) 11-32 (McGraw-Hill, London, 1999).
68. Hofmeyr, S. A. & Forrest, S. in Proc. Genetic and Evolutionary Computation Conf. (eds Banzhaf, W. et al.) 1289-1296 (Orlando, Florida, 1999).
69. Vose, M. D. The Simple Genetic Algorithm: Foundations and Theory (MIT Press, Cambridge, Massachusetts, 1999). A thorough presentation of current genetic algorithm theory.
70. Jones, T. Computer Science. Thesis, Univ. of New Mexico, Albuquerque, New Mexico (1995).
71. Poli, R. & Langdon, W. B. Schema theory for genetic programming with one-point crossover and point mutation. Evol. Comput. 6, 231-252 (1998).
72. Heckendorn, R. B. & Whitley, D. Walsh functions and predicting problem complexity. Evol. Comput. 7, 69-101 (1999).
73. Altenberg, L. in Foundations of Genetic Algorithms III (eds Whitley, L. D. & Vose, M. D.) 23-49 (Morgan Kaufmann, San Francisco, California, 1995).
74. Fogel, D. B. & Ghozeil, A. The schema theorem and the misallccation of trials in genetic algorithms. Inform. Sci. 122, 93-119 (2000).
75. Macready, W. G. & Wolpert, D.H. Bandit problems and the exploration/exploitation trade-off. IEEE Trans. Evol. Comput. 2, 2-22 (1998).