A multi-chromosome approach to standard and embedded cartesian genetic programming

GECCO 2006 - Genetic and Evolutionary Computation Conference

Volumn 1, Issue , 2006, Pages 903-910

  Walker, James Alfred ^a   Miller, Julian Francis ^a   Cavill, Rachel ^a  

^a UNIVERSITY OF YORK   (United Kingdom)

Author keywords

Automatically Defined Functions; Cartesian Genetic Programming; Digital Circuits; Embedded Cartesian Genetic Programming; Evolution; Module Acquisition; Multi chromosome; Multi chromosome Evolutionary Strategy

Indexed keywords

CHROMOSOMES; COMPUTATION THEORY; DIGITAL CIRCUITS; GENETIC ALGORITHMS; PROBLEM SOLVING;

AUTOMATICALLY DEFINED FUNCTIONS; CARTESIAN GENETIC PROGRAMMING; EMBEDDED CARTESIAN GENETIC PROGRAMMING; MODULE ACQUISITION; MULTI-CHROMOSOME EVOLUTIONARY STRATEGY; MULTI-CHROMOSOMES;

EMBEDDED SYSTEMS;

EID: 33750246918     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143997.1144153     Document Type: Conference Paper

References (25)

1. P. J. Angelina and J. Pollack. Evolutionary Module Acquisition. In Proc. of the 2nd Annual Conference on Evolutionary Programming, pages 154-163, La Jolla, CA, USA, 25-26 Feb. 1993.
2. R. Cavill, S. L. Smith, and A. M. Tyrrell. Multi-chromosomal Genetic Programming. In Proc. of the 2005 Genetic and Evolutionary Computation Conference, volume 2, pages 1649-1656, Washington DC, USA, 25-29 June 2005. ACM Press.
3. R. Cavill, S. L. Smith, and A. M. Tyrrell. The Performance of Polyploid Evolutionary Algorithms is Improved Both by Having Many Chromosomes and by Having Many Copies of Each Chromosome on Symbolic Regression Problems. In Proc. of the 2005 Congress on Evolutionary Computation Conference, volume 1, pages 935-941, sept 2005.
4. R. Chow. Genotype to Phenotype Mappings with a Multiple-Chromosome Genetic Algorithm. In Proc. of the 2004 Genetic and Evolutionary Computation Conference, volume 3102 of LNCS, pages 1006-1017, Seattle, WA, USA, 26-30 June 2004. Springer-Verlag.
5. D. Corne, E. Collingwood, and P. Ross. Investigating Multiploidy's Niche. In Evolutionary Computing: Selected Papers from the AISB Workshop, volume 1143 of LNCS, pages 189-197, Brighton, UK, Apr. 1996. Springer-Verlag.
6. T. Haynes, S. Sen, D. Schoenefeld, and R. Wainwright. Evolving a Team. In Working Notes for the AAAI Symposium on Genetic Programming, Cambridge, MA, 1995. AAAI.
7. D. W. Hillis. Co-Evolving Parasites Improve Simulated Evolution in an Optimization Procedure. Physica D, 42:228-234, 1990.
8. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.
9. J. R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge, MA, USA, 1994.
10. S. Luke and L. Spector. Evolving Teamwork and Coordination with Genetic Programming. In Genetic Programming 1996: Proc. of the 1st Annual Conference, pages 150-156, Stanford University, CA, USA, 28-31 1996. MIT Press.
11. H. A. Mayer and M. Spitzlinger. Multi-chromosomal Representations and Chromosome Shuffling in Evolutionary Algorithms. In Proc. of the 2003 Congress on Evolutionary Computation Conference, pages 1145-1149, Canberra, 8-12 Dec. 2003. IEEE Press.
12. J. F. Miller. An Empirical Study of the Efficiency of Learning Boolean Functions using a Cartesian Genetic Programming Approach. In Proc. of the 1999 Genetic and Evolutionary Computation Conference, pages 1135-1142, Orlando, Florida, USA, 1999. Morgan Kaufmann.
13. J. F. Miller and P. Thomson. Cartesian Genetic Programming. In Proc. of the 3rd European Conference on Genetic Programming, volume 1802 of LNCS, pages 121-132, Edinburgh, 15-16 Apr. 2000. Springer-Verlag.
14. M. Oltean and C. Grosan. Evolving Digital Circuits using Multi Expression Programming. In Proc. of the 2004 NASA/DoD Conference on Evolvable Hardware, pages 87-90, Seattle, 24-26 June 2004. IEEE Press.
15. M. Oltean, C. Grosan, and M. Oltean. Encoding Multiple Solutions in a Linear Genetic Programming Chromosome. In International Conference on Computational Science, pages 1281-1288, 2004.
16. M. Oltean, C. Grosan, and M. Oltean. Evolving Digital Circuits for the Knapsack Problem. In Computational Science - ICCS 2004: 4th International Conference, Part III, volume 3038 of LNCS, pages 1257-1264, Krakow, Poland, 6-9 June 2004. Springer-Verlag.
17. P. Collet, E. Lutton, F. Raynal, M. Schoenauer. Polar IFS + Parisian Genetic Programming = Efficient IFS Inverse Problem Solving. Genetic Programming and Evolvable Machines, 1(4):339-361, 2000.
18. T. Soule. Voting Teams: A Cooperative Approach to Non-typical Problems using Genetic Programming. In Proc. of the 1999 Genetic and Evolutionary Computation Conference, pages 916-922, San Francisco, CA, 1999. Morgan Kaufmann.
19. T. Soule. Heterogeneity and Specialization in Evolving Teams. In Proc. of the 2000 Genetic and Evolutionary Computation Conference, pages 778-785, Las Vegas, Nevada, USA, 10-12 July 2000. Morgan Kaufmann.
20. J. Torresen. Evolving Multiplier Circuits by Training Set and Training Vector Partitioning. In Proc. of the 5th International Conference on Evolvable Systems, volume 2606 of LNCS, pages 228-237, Trondheim, Norway, 17-20Mar. 2003. Springer-Verlag.
21. V. K. Vassilev, D. Job, and J. F. Miller. Towards the Automatic Design of More Efficient Digital Circuits. In Proc. of the 2nd NASA/DoD Workshop on Evolvable Hardware, pages 151-160, Los Alamitos, California, USA, 2001. IEEE Computer Society.
22. J. A. Walker and J. F. Miller. Evolution and Acquisition of Modules in Cartesian Genetic Programming. In Proc. of the 7th European Conference on Genetic Programming, volume 3003 of LNCS, pages 187-197, Coimbra, Portugal, 5-7 Apr. 2004. Springer-Verlag.
23. J. A. Walker and J. F. Miller. Improving the Evolvability of Digital Multipliers using Embedded Cartesian Genetic Programming and Product Reduction. In Proc. of the 2005 International Conference on Evolvable Systems, volume 3637 of LNCS, pages 131-142, Sitges, Spain, 12-14 Sept. 2005. Springer-Verlag.
24. J. A. Walker and J. F. Miller. Investigating the Performance of Module Acquisition in Cartesian Genetic Programming. In Proc. of the 2005 Genetic and Evolutionary Computation Conference, volume 2, pages 1649-1656, Washington DC, USA, 25-29 June 2005. ACM Press.
25. T. Yu and J. F. Miller. Neutrality and the Evolvability of Boolean Function Landscape. In Proc. of the 4th European Conference on Genetic Programming, volume 2038 of LNCS, pages 204-217, Lake Como, Italy, 18-20 Apr. 2001. Springer-Verlag.