Representations for evolutionary algorithms
Pages 726 - 746
References
[1]
Rothlauf, F. (2006). Representations for Genetic and Evolutionary Algorithms. Springer, Berlin.
[2]
Rothlauf, F. (2011). Design of Modern Heuristics. Springer, Berlin.
[3]
Bäck, T., Michalewicz, Z., and Yao, X., editors (1997). Proceedings of the Fourth International Conference on Evolutionary Computation, Piscataway. IEEE Service Center.
[4]
Barnett, L. (1997). Tangled webs: Evolutionary dynamics on fitness landscapes with neutrality. Master's thesis, School of Cognitive Sciences, University of East Sussex, Brighton.
[5]
Barnett, L. (1998). Ruggedness and neutrality: The NKp family of fitness landscapes. In Adami, C., Belew, R. K., Kitano, H., and Taylor, C. E., editors, Proceedings of the 6th International Conference on Artificial Life (ALIFE-98), pages 18--27, Cambridge. MIT Press.
[6]
Barnett, L. (2001). Netcrawling - optimal evolutionary search with neutral networks. In (Kim et al., 2001), pages 30--37.
[7]
Beasley, D., Bull, D. R., and Martin, R. R. (1993). Reducing epitasis in combinatorial problems by expansive coding. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 400--407, San Francisco. Morgan Kaufmann.
[8]
Choi, S.-S. and Moon, B.-R. (2003). Normalization in genetic algorithms. In Cantú-Paz, E., Foster, J. A., Deb, K., Davis, D., Roy, R., O'Reilly, U.-M., Beyer, H.-G., Standish, R., Kendall, G., Wilson, S., Harman, M., Wegener, J., Dasgupta, D., Potter, M. A., Schultz, A. C., Dowsland, K., Jonoska, N., and Miller, J., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2003, pages 862--873, Heidelberg. Springer.
[9]
Choi, S.-S. and Moon, B.-R. (2008). Normalization for genetic algorithms with nonsynonymously redundant encodings. IEEE Trans. Evolutionary Computation, 12(5):604--616.
[10]
Cohoon, J. P., Hegde, S. U., Martin, W. N., and Richards, D. (1988). Floorplan design using distributed genetic algorithms. In IEEE International Conference on Computer Aided-Design, pages 452--455, Piscataway. IEEE.
[11]
Daida, J. M., Bertram, R., Stanhope, S., Khoo, J., Chaudhary, S., Chaudhri, O., and Polito, J. (2001). What makes a problem GP-hard? Analysis of a tunably difficult problem in genetic programming. Genetic Programming and Evolvable Machines, 2(2):165--191.
[12]
Davis, L. (1989). Adapting operator probabilities in genetic algorithms. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 61--69, Burlington. Morgan Kaufmann.
[13]
Ebner, M., Langguth, P., Albert, J., Shackleton, M., and Shipman, R. (2001). On neutral networks and evolvability. In (Kim et al., 2001), pages 1--8.
[14]
Eshelman, L. J. and Schaffer, J. D. (1991). Preventing premature convergence in genetic algorithms by preventing incest. In Belew, R. K. and Booker, L. B., editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 115--122, Burlington. Morgan Kaufmann.
[15]
Feller, W. (1957). An Introduction to Probability Theory and its Applications, volume 1. John Wiley & Sons, New York, 1st edition.
[16]
Fogel, D. B. and Stayton, L. C. (1994). On the effectiveness of crossover in simulated evolutionary optimization. BioSystems, 32:171--182.
[17]
Fonseca, C., Kim, J.-H., and Smith, A., editors (2000). Proceedings of 2000 IEEE Congress on Evolutionary Computation, Piscataway. IEEE Press.
[18]
Fox, B. R. and McMahon, M. B. (1991). Genetic operators for sequencing problems. In Rawlins, G. J. E., editor, Foundations of Genetic Algorithms, pages 284--300, San Mateo. Morgan Kaufmann.
[19]
Gerrits, M. and Hogeweg, P. (1991). Redundant coding of an NP-complete problem allows effective genetic algorithm search. In Schwefel, H.-P. and Männer, R., editors, Parallel Problem Solving from Nature - PPSN I, volume 496 of LNCS, pages 70--74, Berlin. Springer.
[20]
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading.
[21]
Goldberg, D. E. (1991). Real-coded genetic algorithms, virtual alphabets, and blocking. Complex Systems, 5(2):139--167.
[22]
Harik, G. R., Cantú-Paz, E., Goldberg, D. E., and Miller, B. L. (1997). The gambler's ruin problem, genetic algorithms, and the sizing of populations. In (Bäck et al., 1997), pages 7--12.
[23]
Hoai, N. X., McKay, R. I., and Essam, D. L. (2006). Representation and structural difficulty in genetic programming. IEEE Trans. Evolutionary Computation, 10(2):157--166.
[24]
Jones, T. and Forrest, S. (1995). Fitness distance correlation as a measure of problem difficulty for genetic algorithms. Proceedings of the Sixth International Conference on Genetic Algorithms, pages 184--192.
[25]
Julstrom, B. A. (1999). Redundant genetic encodings may not be harmful. In Banzhaf, W., Daida, J., Eiben, A. E., Garzon, M. H., Honavar, V., Jakiela, M., and Smith, R. E., editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '99, page 791, Burlington. Morgan Kaufmann.
[26]
Kim, J.-H., Zhang, B.-T., Fogel, G., and Kuscu, I., editors (2001). Proceedings of 2001 IEEE Congress on Evolutionary Computation, Piscataway. IEEE Press.
[27]
Kimura, M. (1983). The Neutral Theory of Molecular Evolution. Cambridge University Press.
[28]
Knowles, J. D. and Watson, R. A. (2002). On the utility of redundant encodings in mutationbased evolutionary search. In Merelo, J. J., Adamidis, P., Beyer, H.-G., Fernandez-Villacanas, J.-L., and Schwefel, H.-P., editors, Parallel Problem Solving from Nature-PPSN VII, pages 88--98, Berlin. Springer.
[29]
Liepins, G. E. and Vose, M. D. (1990). Representational issues in genetic optimization. Journal of Experimental and Theoretical Artificial Intelligence, 2:101--115.
[30]
Moraglio, A. and Poli, R. (2004). Topological interpretation of crossover. In Deb, K., Poli, R., Banzhaf, W., Beyer, H.-G., Burke, E., Darwen, P., Dasgupta, D., Floreano, D., Foster, J., Harman, M., Holland, O., Lanzi, P. L., Spector, L., Tettamanzi, A., Thierens, D., and Tyrrell, A., editors, gecco2004, pages 1377--1388, Heidelberg. Springer.
[31]
Palmer, C. C. (1994). An approach to a problem in network design using genetic algorithms. PhD thesis, Polytechnic University, Brooklyn, NY.
[32]
Radcliffe, N. J. (1992). Non-linear genetic representations. In Männer, R. and Manderick, B., editors, Parallel Problem Solving from Nature - PPSN II, pages 259--268, Berlin. Springer.
[33]
Radcliffe, N. J. (1997). Theoretical foundations and properties of evolutionary computations: Schema processing. In Bäck, T., Fogel, D. B., and Michalewicz, Z., editors, Handbook of Evolutionary Computation, pages B2.5:1--B2.5:10. Institute of Physics Publishing and Oxford University Press, Bristol and New York.
[34]
Raidl, G. R. (2000). An efficient evolutionary algorithm for the degree-constrained minimum spanning tree problem. In (Fonseca et al., 2000), pages 43--48.
[35]
Reeves, C. R. (2000). Fitness landscapes: A guided tour. Joint tutorials of SAB 2000 and PPSN 2000, tutorial handbook.
[36]
Ronald, S. (1997). Robust encodings in genetic algorithms: A survey of encoding issues. In (Bäcket al., 1997), pages 43--48.
[37]
Ronald, S., Asenstorfer, J., and Vincent, M. (1995). Representational redundancy in evolutionary algorithms. In Fogel, D. B. and Attikiouzel, Y., editors, Proceedings of the 1995 IEEE International Conference on Evolutionary Computation, volume 2, pages 631--636, Piscataway. IEEE Service Center.
[38]
Rothlauf, F. (2002). Representations for Genetic and Evolutionary Algorithms. Number 104 in Studies on Fuzziness and Soft Computing. Springer, Heidelberg, 1st edition.
[39]
Rothlauf, F. and Goldberg, D. E. (2003). Redundant representations in evolutionary computation. Evolutionary Computation, 11(4):381--415.
[40]
Rowe, J. E., Vose, M. D., and Wright, A. H. (2010). Representation invariant genetic operators. Evolutionary Computation, 18(4):635--660.
[41]
Shackleton, M., Shipman, R., and Ebner, M. (2000). An investigation of redundant genotypephenotype mappings and their role in evolutionary search. In (Fonseca et al., 2000), pages 493--500.
[42]
Shipman, R. (1999). Genetic redundancy: Desirable or problematic for evolutionary adaptation? In Dobnikar, A., Steele, N. C., Pearson, D. W., and Albrecht, R. F., editors, Proceedings of the 4th International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA), pages 1--11, Berlin. Springer.
[43]
Shipman, R., Shackleton, M., Ebner, M., and Watson, R. (2000a). Neutral search spaces for artificial evolution: A lesson from life. In Bedau, M., McCaskill, J., Packard, N., and Rasmussen, S., editors, Proceedings of Artificial Life VII, page section III (Evolutionary and Adaptive Dynamics). MIT Press.
[44]
Shipman, R., Shackleton, M., and Harvey, L. (2000b). The use of neutral genotypephenotype mappings for improved evoutionary search. British Telecom Technology Journal, 18(4):103--111.
[45]
Smith, T., Husbands, P., and O'Shea, M. (2001a). Evolvability, neutrality and search space. Technical Report 535, School of Cognitive and Computing Sciences, University of Sussex.
[46]
Smith, T., Husbands, P., and O'Shea, M. (2001b). Neutral networks and evolvability with complex genotype-phenotype mapping. In Proceedings of the European Converence on Artificial Life: ECAL2001, volume LNAI 2159, pages 272--281, Berlin. Springer.
[47]
Smith, T., Husbands, P., and O'Shea, M. (2001c). Neutral networks in an evolutionary robotics search space. In (Kim et al., 2001), pages 136--145.
[48]
Surry, P. D. and Radcliffe, N. (1996). Formal algorithms + formal representations = search strategies. In Voigt, H.-M., Ebeling, W., Rechenberg, I., and Schwefel, H.-P., editors, Parallel Problem Solving from Nature - PPSN IV, pages 366--375, Berlin. Springer.
[49]
Thorhauer (2016): On the Non-uniform Redundancy in Grammatical Evolution. In PPSN 2016. pages 292--302. Berlin. Springer
[50]
Thorhauer, Ann; Rothlauf, Franz: On the Locality of Standard Search Operators in Grammatical Evolution. In: Thomas Bartz-Beielstein and Juergen Brank and Jim Smith editors, 13th International Conference on Parallel Problem Solving from Nature (PPSN 2014), Ljubljana, Slovenia, 2014.
[51]
Toussaint, M. and Igel, C. (2002). Neutrality: A necessity for self-adaptation. In Fogel, D. B., El-Sharkawi, M. A., Yao, X., Greenwood, G., Iba, H., Marrow, P., and Shackleton, M., editors, Proceedings of 2002 IEEE Congress on Evolutionary Computation, pages 1354--1359, Piscataway. IEEE Press.
[52]
Watson, R. A., Hornby, G. S., and Pollack, J. B. (1998). Modeling building-block interdependency. In Eiben, A. E., Bäck, T., Schoenauer, M., and Schwefel, H.-P., editors, Parallel Problem Solving from Nature - PPSN V, volume 1498 of LNCS, pages 97--106, Berlin. Springer.
[53]
Whitley, L. D. (2000). Walsh analysis, schemata, embedded landscapes and no free lunch. Joint Tutorials of SAB 2000 and PPSN 2000.
[54]
Yu, T. and Miller, J. (2001). Neutrality and evolvability of Boolean function landscapes. In Miller, J., Tomassini, M., Lanzi, P. L., Ryan, C., Tetamanzi, A. G. B., and Langdon, W. B., editors, Proceedings of the Fourth European Conference on Genetic Programming (EuroGP-2001), volume 2038 of LNCS, pages 204--217, Berlin. Springer.
[55]
Yu, T. and Miller, J. (2002). Finding needles in haystacks is not hard with neutrality. In Foster, J. A., Lutton, E., Miller, J., Ryan, C., and Tettamanzi, A. G. B., editors, Proceedings of the Fifth European Conference on Genetic Programming (EuroGP- 2002), volume 2278 of LNCS, pages 13--25, Berlin. Springer.
Recommendations
Use of the q-Gaussian mutation in evolutionary algorithms
Special issue on advances in computational intelligence and bioinformaticsThis paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed ...
Using holey fitness landscapes to counteract premature convergence in evolutionary algorithms
GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computationPremature convergence is a persisting problem in evolutionary optimisation, in particular - genetic algorithms. While a number of methods exist to approach this issue, they usually require problem specific calibration or only partially resolve the issue,...
Representations for Evolutionary Algorithms
GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary ComputationSuccessful and efficient use of evolutionary algorithms (EA) depends on the choice of the genotype, the problem representation (mapping from genotype to phenotype) and on the choice of search operators that are applied to the genotypes. These choices ...
Comments
Information & Contributors
Information
Published In
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
- Editor:
- Manuel López-Ibáñez,
- General Chairs:
- Anne Auger,
- Thomas Stützle
Copyright © 2019 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 July 2019
Check for updates
Qualifiers
- Tutorial
Conference
GECCO '19
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 39Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 01 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in