Introducing start expression genes to the linkage learning genetic algorithm

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2439, Issue , 2002, Pages 351-360

  Chen, Ying Ping ^a   Goldberg, David E ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GENES; GENETIC ALGORITHMS;

BUILDING BLOCKES; EXCHANGE CROSSOVER; LINKAGE LEARNING;

LEARNING ALGORITHMS;

EID: 84944328234     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45712-7_34     Document Type: Conference Paper

References (16)

1. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI (1975)
2. Harik, G.R., Goldberg, D.E.: Learning linkage. FOGA 4 (1996) 247–262
3. Harik, G.R.: Learning Gene Linkage to Efficiently Solve Problems of Bounded Difficulty Using Genetic Algorithms. Unpublished doctoral dissertation, University of Michigan, Ann Arbor, MI (1997) (Also IlliGAL Report No. 97005).
4. Harik, G.R., Goldberg, D.E.: Learning linkage through probabilistic expression. Computer Methods in Applied Mechanics and Engineering 186 (2000) 295–310
5. Nordin, P., Francone, F., Banzhaf, W.: Explicitly defined introns and destructive crossover in genetic programming. In: Proceedings of the Workshop on GP: From Theory to Real-World Applications. (1995) 6–22
6. Andre, D., Teller, A.: A study in program response and the negative effects of introns in genetic programming. In: Proceedings of GP-96. (1996) 12–20
7. Haynes, T.: Duplication of coding segments in genetic programming. In: Proceedings of NCAI-96. (1996) 344–349
8. Lindsay, R.K., Wu, A.S.: Testing the robustness of the genetic algorithm on the floating building block representation. In: AAAI/IAAI, Vol. 1. (1996) 793–798
9. Wineberg, M., Oppacher, F.: The benefits of computing with introns. In: Proceedings of GP-96. (1996) 410–415
10. Wu, A.S., Lindsay, R.K.: A survey of intron research in genetics. PPSN IV (1996) 101–110
11. Levenick, J.R.: Swappers: Introns promote flexibility, diversity and invention. In: Proceedings of GECCO-99. (1999) 361–368
12. Iba, H., Terao, M.: Controlling effective introns for multi-agent learning by genetic programming. In: Proceedings of GECCO-2000. (2000) 419–426
13. Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Boston, MA (2002)
14. Thierens, D., Goldberg, D.E.: Mixing in genetic algorithms. In: Proceedings of ICGA-93. (1993) 38–45
15. Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. FOGA 2 (1993) 93–108
16. De Jong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Unpublished doctoral dissertation, University of Michigan, Ann Arbor, MI (1975) (University Microfilms No. 76-9381).