Abstract
The Age-Layered Population Structure (ALPS) paradigm is a novel metaheuristic for overcoming premature convergence by running multiple instances of a search algorithm simultaneously. When the ALPS paradigm was first introduced it was combined with a generational Evolutionary Algorithm (EA) and the ALPS-EA was shown to work significantly better than a basic EA. Here we describe a version of ALPS with a steady-state EA, which is well suited for use in situations in which the synchronization constraints of a generational model are not desired. To demonstrate the effectiveness of our version of ALPS we compare it against a basic steady-state EA (BEA) in two test problems and find that it outperforms the BEA in both cases.
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References
Cantú-Paz, E. and Goldberg, D. E. (2003). Are multiple runs of genetic algorithms better than one? In et al., E. Cantu-Paz, editor, Proc. of the Genetic and Evolutionary Computation Conference, LNCS 2724, pages 801–812, Berlin. Springer-Verlag.
Cavicchio, D. J. (1970). Adaptive Search using simulated evolution. PhD thesis, University of Michigan, Ann Arbor.
DeJong, K. A. (1975). Analysis of the Behavior of a Class of Genetic Adaptive Systems. Dept. Computer and Communication Sciences, University of Michigan, Ann Arbor.
Goldberg, David E. and Richardson, Jon (1987). Genetic algorithms with sharing for multimodal function optimization. In Grefenstette, John J., editor, Proc. of the Second Intl. Conf. on Genetic Algorithms, pages 41–49. Lawrence Erlbaum Associates.
Hornby, Gregory S. (2006). ALPS: the age-layered population structure for reducing the problem of premature convergence. In Keijzer, Maarten, Cattolico, Mike, Arnold, Dirk, Babovic, Vladan, Blum, Christian, Bosman, Peter, Butz, Martin V., Coello Coello, Carlos, Dasgupta, Dipankar, Ficici, Sevan G., Foster, James, Hernandez-Aguirre, Arturo, Hornby, Greg, Lipson, Hod, McMinn, Phil, Moore, Jason, Raidl, Guenther, Rothlauf, Franz, Ryan, Conor, and Thierens, Dirk, editors, GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, volume 1, pages 815–822, Seattle, Washington, USA. ACM Press.
Hornby, Gregory S., Lipson, Hod, and Pollack, Jordan B. (2003). Generative representations for the automated design of modular physical robots. IEEE transactions on Robotics and Automation, 19(4):709–713.
Huber, A. and Mlynski, D. A. (1998). An age-controlled evolutionary algorithm for optimization problems in physical layout. In International Symposium on Circuits and Systems, pages 262–265. IEEE Press.
Kim, J.-H., Jeon, J.-Y., Chae, H.-K., and Koh, K. (1995). A novel evolutionary algorithm with fast convergence. In IEEE International Conference on Evolutionary Computation, pages 228–29. IEEE Press.
Kirkpatrick, S., Gelatt, C.D., and Vecchi, M.P. (1983). Optimization by simulated annealing. Science, 220:671–680.
Korns, M. F. and Nunez, L. (2008). Profiling symbolic regression-classification. In Riolo, R. L., Soule, T., and Worzel, B., editors, Genetic Programming Theory and Practice VI, Genetic and Evolutionary Computation, chapter 14, pages 215–229. Springer, Ann Arbor.
Kubota, N., Fukuda, T., Arai, F., and Shimojima, K. (1994). Genetic algorithm with age structure and its application to self-organizing manufacturing system. In IEEE Symposium on Emerging Technologies and Factory Automation, pages 472–477. IEEE Press.
Lohn, Jason D., Hornby, Gregory S., and Linden, Derek S. (2005). Rapid reevolution of an X-band antenna for NASA's space technology 5 mission. In Yu, Tina, Riolo, Rick L., and Worzel, Bill, editors, Genetic Programming Theory and Practice III, volume 9 of Genetic Programming, chapter 5, pages 65–78. Springer, Ann Arbor.
Luke, Sean (2001). When short runs beat long runs. In Spector, Lee, Goodman, Erik D., Wu, Annie, Langdon, W. B., Voigt, Hans-Michael, Gen, Mitsuo, Sen, Sandip, Dorigo, Marco, Pezeshk, Shahram, Garzon, Max H. and Burke, Edmund, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 74–80, San Francisco, California, USA. Morgan Kaufmann.
Mahfoud, S. W. (1992). Crowding and preselection revisited. In Männer, R. and Manderick, B., editors, Parallel Problem Solving from Nature, 2, pages 27–36. North-Holland.
McConaghy, Trent, Palmers, Pieter, Gielen, Georges, and Steyaert, Michiel (2007). Genetic programming with reuse of known designs. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice V, Genetic and Evolutionary Computation, chapter 10, pages 161–186. Springer, Ann Arbor.
Willis, A., Patel, S., and Clack, C. D. (2008). GP age-layer and crossover effects in bid-offer spread prediction. In Proceedings of the 10th annual conference on Genetic and Evolutionary Computation Conference, Atlanta, GA.
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Hornby, G.S. (2010). A Steady-State Version of the Age-Layered Population Structure EA. In: Riolo, R., O'Reilly, UM., McConaghy, T. (eds) Genetic Programming Theory and Practice VII. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1626-6_6
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DOI: https://doi.org/10.1007/978-1-4419-1626-6_6
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