Parallel evolution using multi-chromosome cartesian genetic programming

Genetic Programming and Evolvable Machines

Volumn 10, Issue 4, 2009, Pages 417-445

  Walker, James Alfred ^a   Völk, Katharina ^a   Smith, Stephen L ^a   Miller, Julian Francis ^a  

^a UNIVERSITY OF YORK   (United Kingdom)

Author keywords

Cartesian genetic programming; Digital circuits; Mammography; Multiple chromosomes; Parallelisation

Indexed keywords

CARTESIAN GENETIC PROGRAMMING; DIGITAL CIRCUIT DESIGN; DISTRIBUTED METHODS; MULTIPLE CHROMOSOMES; MULTIPLE OUTPUTS; MUTATION OPERATORS; PARALLELISATION; POTENTIAL SOLUTIONS; REGIONS OF INTEREST; SINGLE OBJECTIVE; SUBIMAGES;

DIGITAL INTEGRATED CIRCUITS; EVOLUTIONARY ALGORITHMS; GENETIC PROGRAMMING; IMAGE ANALYSIS; IMAGE CLASSIFICATION; INTEGRATED CIRCUIT MANUFACTURE; MATHEMATICAL OPERATORS; PARALLEL ALGORITHMS; PARALLEL PROGRAMMING;

CHROMOSOMES;

EID: 70449535716     PISSN: 13892576     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10710-009-9093-2     Document Type: Article

References (49)

1. Center for Health Care Technologies Livermore. Lawrence Livermore National Library/UCSF Digital Mammogram Database. Livermore, CA, 1995
2. D. Andre, A. Teller, Evolving team darwin united, in RoboCup-98: Robot Soccer World Cup II, ed. by M. Asada, H. Kitano (Springer, 1999), pp. 346-351
3. R. Cavill, Multi-chromosomal genetic programming. PhD thesis, Department of Electronics, University of York, UK, 2006
4. R. Cavill, S.L. Smith, A.M. Tyrrell, Multi-chromosomal genetic programming, in Proceedings of the 2005 Genetic and Evolutionary Computation Conference (GECCO), vol. 2 (ACM Press, Washington DC, 2005), pp. 1649-1656
5. R. Cavill, S.L. Smith, 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 Proceedings of the 2005 Congress on Evolutionary Computation Conference (CEC), vol. 1 (2005), pp. 935-941
6. R. Chow, Genotype to phenotype mappings with a multiple-chromosome genetic algorithm, in Proceedings of the 2004 Genetic and Evolutionary Computation Conference (GECCO), vol. 3102 of LNCS (Springer, Seattle, 2004), pp. 1006-1017
7. S. Christensen, F. Oppacher, An analysis of koza's computational effort statistic for genetic programming, in Proceedings of the 5th European Conference on Genetic Programming (EuroGP), vol. 2278 of Lecture notes in computer science (Springer, 2002), pp. 182-191
8. J. Clegg, J.A. Walker, J.F. Miller, A new crossover technique for cartesian genetic programming, in Proceedings of the 2007 Genetic and Evolutionary Computation Conference (GECCO) (ACM Press, 2007), pp. 1580-1587
9. P. Collet E. Lutton F. Raynal M. Schoenauer 2000 Polar IFS + parisian genetic programming = efficient IFS inverse problem solving Genet. Program. Evolvable Mach. 1 4 339 361 1060.68570 10.1023/A:1010065123132
10. D. Corne, E. Collingwood, P. Ross, Investigating multiploidy's niche, in Evolutionary computing: selected papers from the AISB workshop, vol. 1143 of LNCS (Springer, Brighton, 1996), pp. 189-197
11. D.B. Fogel, Evolving artificial intelligence. PhD thesis, University of California, San Diego (1992)
12. L.J. Fogel, A.J. Owens, M.J. Walsh, Artificial Intelligence Through Simulated Evolution (Wiley, New York, 1966)
13. J.C. Fu, et al. 2005 Image segmentation feature selection and pattern classification for mammographic microcalcifications Comput. Med. Imaging. Graph. 29 6 419 429 10.1016/j.compmedimag.2005.03.002 (Pubitemid 41247787)
14. T. Haynes, S. Sen, D. Schoenefeld, R. Wainwright, in Working Notes for the AAAI Symposium on Genetic Programming. Evolving a Team (Cambridge, AAAI, 1995)
15. D.W. Hillis 1990 Co-evolving parasites improve simulated evolution in an optimization procedure Physica D 42 228 234 10.1016/0167-2789(90)90076-2
16. J. Jiang B. Yao A. Wason 2007 A genetic algorithm design for microcalcification detection and classification in digital mammograms Comput. Med. Imaging. Graph. 31 1 49 61 10.1016/j.compmedimag.2006.09.011 (Pubitemid 44879885)
17. J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, 1992)
18. S. Lee C. Lo C. Wang P. Chung C. Chang C. Yang P. Hsu 2000 A computer-aided design mammography screening system for detection and classification of microcalcifications Int. J. Med. Inform. 60 1 29 57 10.1016/S1386-5056(00)00067-8 (Pubitemid 30676376)
19. S. Luke, C. Hohn, J. Farris, G. Jackson, J. Hendler, in RoboCup-97: Robot Soccer World Cup I, ed. by H. Kitano. Co-evolving soccer softbot team coordination with genetic programming (Springer, New York, 1997), pp. 398-411
20. S. Luke, L. Spector, in Proceedings of the 1st Annual Workshop on Genetic Programming (GP). Evolving Teamwork and Coordination with Genetic Programming (MIT Press, Stanford University, CA, 28-31, 1996), pp. 150-156
21. H.B. Mann D.R. Whitney 1947 On a test of whether one of 2 random variables is stochastically larger than the other Ann. Math. Stat. 18 50 60 0041.26103 10.1214/aoms/1177730491 22058
22. H.A. Mayer, M. Spitzlinger, in Proceedings of the 2003 Congress on Evolutionary Computation Conference (CEC). Multi-chromosomal Representations and Chromosome Shuffling in Evolutionary Algorithms (IEEE Press, Canberra, 2003), pp. 1145-1149
23. J.F. Miller, in Proceedings of the 1999 Genetic and Evolutionary Computation Conference (GECCO). An Empirical Study of the Efficiency of Learning Boolean Functions using a Cartesian Genetic Programming Approach (Morgan Kaufmann, Orlando, 1999), pp. 1135-1142
24. J.F. Miller D. Job V.K. Vassilev 2000 Principles in the evolutionary design of digital circuits-part I Genet. Program. Evolvable Mach. 1 1 8 35 10.1023/A:1010016313373
25. J.F. Miller S.L. Smith 2006 Redundancy and computational efficiency in cartesian genetic programming IEEE Trans. Evol. Comput. 10 2 167 174 10.1109/TEVC.2006.871253
26. J.F. Miller, P. Thomson, in Proceedings of the 3rd European Conference on Genetic Programming (EuroGP 2000), vol. 1802 of Lecture Notes in Computer Science. Cartesian Genetic Programming (Springer, Edinburgh, 2000), pp. 121-132
27. J.F. Miller, P. Thomson, T.C. Fogarty, in Genetic Algorithms and Evolution Strategies in Engineering and Computer Science: Recent Advancements and Industrial Applications. Designing Electronic Circuits Using Evolutionary Algorithms. Arithmetic Circuits: A Case Study (1997)
28. J. Niehaus, W. Banzhaf, in Proceedings of the Fifth European Conference on Genetic Programming (EuroGP), vol. 2610 of Lecture Notes in Computer Science. More on Computational Effort Statistics for Genetic Programming (Springer, 2003), pp. 164-172
29. R. Nishikawa 2007 Current status and future directions of computer-aided diagnosis in mammography Comput. Med. Imaging Graph. 31 224 235 10.1016/j.compmedimag.2007.02.009 (Pubitemid 46601782)
30. M. Oltean, C. Grosan, in Proceedings of the 2004 NASA/DoD Conference on Evolvable Hardware. Evolving Digital Circuits Using Multi Expression Programming (IEEE Press, Seattle, 2004), pp. 87-90
31. M. Oltean, C. Grosan, M. Oltean, in International Conference on Computational Science. Encoding Multiple Solutions in a Linear Genetic Programming Chromosome (2004), pp. 1281-1288
32. M. Oltean, C. Grosan, M. Oltean, in Proceedings of the 4th International Conference on Computational Science (ICCS), vol. 3038 of LNCS. Evolving Digital Circuits for the Knapsack Problem (Springer, Krakow 2004), pp. 1257-1264
33. C. Ryan, in In AAAI Fall Symposium Series on Genetic Programming Working Notes. GP Robots and GP Teams: Competition, Co-evolution and Co-operation in Genetic Programming (AAAI, 1995), pp. 86-93
34. H. Schwefel, Kybernetische evolution als strategie der experimentelen forschung in der stromungstechnik. Master's thesis, Technical University Berlin (1965)
35. T. Soule, in Proceedings of the 1999 Genetic and Evolutionary Computation Conference (GECCO). Voting Teams: A Cooperative Approach to Non-typical Problems Using Genetic Programming (Morgan Kaufmann, San Francisco, 1999), pp. 916-922
36. T. Soule, in Proceedings of the 2000 Genetic and Evolutionary Computation Conference (GECCO). Heterogeneity and Specialization in Evolving Teams, pp. 778-785
37. J. Torresen, in Proceedings of the 5th International Conference on Evolvable Systems (ICES), vol. 2606 of Lecture Notes in Computer Science. Evolving Multiplier Circuits by Training set and Training Vector Partitioning (Springer, Trondheim, 2003), pp. 228-237
38. C. Van Rijsbergen. Information Retrieval. Butterworths (1979)
39. V.K. Vassilev, J.F. Miller, in Proceedings of the 3rd International Conference on Evolvable Systems (ICES), vol. 1801 of Lecture Notes in Computer Science. The Advantages of Landscape Neutrality in Digital Circuit Evolution (Springer, 2000), pp. 252-263
40. G. Veni, E. Regentova, L. Zhang, Detection of clustered microcalcifications with susan edge detector, adaptive contrast thresholding and spatial filters, vol. 5112 of LNCS, pp. 837-843 (2008)
41. J.A. Walker, J.F. Miller, in Proceedings of the 7th European Conference on Genetic Programming (EuroGP), vol. 3003 of Lecture Notes in Computer Science. Evolution and acquisition of modules in cartesian genetic programming (Springer, 2004), pp. 187-197
42. J.A. Walker, J.F. Miller, in Proceedings of the 2005 International Conference on Evolvable Systems (ICES), vol. 3637 of Lecture Notes in Computer Science. Improving the Evolvability of Digital Multipliers using Embedded Cartesian Genetic Programming and Product Reduction (Springer, 2005), pp. 131-142
43. J.A. Walker, J.F. Miller, in Proceedings of the 2005 Genetic and Evoluationary Computation Conference (GECCO), vol. 2. Investigating the Performance of Module Acquisition in Cartesian Genetic Programming (ACM Press, 2005), pp 1649-1656
44. J.A. Walker, J.F. Miller. Automatic acquisition, evolution and re-use of modules in cartesian genetic programming. to be published in IEEE Trans. Evol. Comput. (2008)
45. M. Walker, H. Edwards, C. Messom, in Proceedings of the 10th European Conference on Genetic Programming (EuroGP), vol. 4445 of Lecture Notes in Computer Science. Confidence intervals for computational effort comparisons (Springer, 2007), pp. 23-32
46. M. Walker, H. Edwards, C. Messom, in Proceedings of the 2007 Genetic and Evolutionary Computation Conference (GECCO). The Reliability of Confidence Intervals for Computational Effort Comparisons (ACM, 2007), pp. 1716-1723
47. F. Wilcoxon 1945 Individual comparisons by ranking methods. Biomet. Bull. 1 80 83 10.2307/3001968
48. T. Yu, J.F. Miller, in Proceedings of the 4th European Conference on Genetic Programming (EuroGP), vol. 2038 of Lecture Notes in Computer Science. Neutrality and the Evolvability of Boolean Function Landscape (Springer, 2001), pp. 204-217
49. T. Yu, J.F. Miller, in Proceedings of the 5th European Conference on Genetic Programming (EuroGP), vol. 2278 of Lecture Notes in Computer Science. Finding needles in haystacks is not hard with neutrality (Springer, 2002), pp. 13-25