Abstract
It has been proven that non-elitist evolutionary algorithms (EAs) with proper selection mechanisms, including the recently proposed power-law ranking selection, can efficiently escape local optima on a broad class of problems called SparseLocalOpt \(_{\alpha ,\varepsilon }\), where elitist EAs fail. However, those theoretical upper bounds on the runtime are not tight as they require large populations and a tight balance between mutation rates and selection pressure to keep the algorithms operating near the so-called “error threshold”. This paper empirically clarifies the significance of these theoretical requirements and makes a series of performance comparisons between the non-elitist EA using power-law ranking selection and other EAs on various benchmark problems.
Our experimental results show that non-elitist EAs optimise the Funnel problem with deceptive local optimum significantly faster with power-law ranking selection than with tournament selection. Furthermore, power-law selection outperforms UMDA and the (1+1) EA in our experiments on the NK-Landscape and Max k-Sat problems, but yields to the \((\mu ,\lambda )\)-selection, tournament selection, and the self-adaptive MOSA-EA. On the unicost set cover problems, the EA with power-law selection shows competitive results.
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References
Baker, J.E.: An analysis of the effects of selection in genetic algorithms. Ph.D. thesis, Vanderbilt University (1989)
Beasley, J.E.: OR-Library: distributing test problems by electronic mail. J. Oper. Res. Soc. 41(11), 1069–1072 (1990)
Buzdalov, M., Doerr, B.: Runtime analysis of the \((1 + (\lambda ,\lambda ))\) genetic algorithm on random satisfiable 3-CNF formulas. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1343–1350. ACM, Berlin Germany (2017)
Corus, D., Dang, D.C., Eremeev, A.V., Lehre, P.K.: Level-based analysis of genetic algorithms and other search processes. IEEE Trans. Evol. Comput. 22(5), 707–719 (2018)
Dang, D.C., Eremeev, A., Lehre, P.K.: escaping local optima with non-elitist evolutionary algorithms. In: Proceedings of AAAI 2021. AAAI Press, Palo Alto, California USA (2020)
Dang, D.C., Eremeev, A., Lehre, P.K.: Non-elitist evolutionary algorithms excel in fitness landscapes with sparse deceptive regions and dense valleys. In: Proceedings of the GECCO, pp. 1133–1141. GECCO ’21, ACM, New York, NY, USA (2021)
Dang, D.C., Eremeev, A., Lehre, P.K., Qin, X.: Fast non-elitist evolutionary algorithms with power-law ranking selection. In: Proceedings of GECCO, pp. 1372–1380. GECCO ’22, ACM, New York, NY, USA (2022)
Devroye, L.: Non-Uniform Random Variate Generation. Springer-Verlag, New York, NY, USA (1986). https://doi.org/10.1007/978-1-4613-8643-8
Doerr, B., Doerr, C., Ebel, F.: From black-box complexity to designing new genetic algorithms. Theoret. Comput. Sci. 567, 87–104 (2015)
Doerr, B., Le, H.P., Makhmara, R., Nguyen, T.D.: Fast genetic algorithms. In: Proceedings of the GECCO, pp. 777–784. ACM, Berlin Germany (2017)
Gao, C., Yao, X., Weise, T., Li, J.: An efficient local search heuristic with row weighting for the unicost set covering problem. Eur. J. Oper. Res. 246(3), 750–761 (2015)
Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: Proceedings of the First Workshop on Foundations of Genetic Algorithms, pp. 69–93. Morgan Kaufmann (1990)
Harik, G., Lobo, F., Goldberg, D.: The compact genetic algorithm. IEEE Trans. Evol. Comput. 3(4), 287–297 (1999)
Hevia Fajardo, M.A.H., Sudholt, D.: Self-adjusting population sizes for non-elitist evolutionary algorithms: why success rates matter. In: Proceedings of GECCO, pp. 1151–1159. ACM, Lille France (2021)
Lehre, P.K.: Negative drift in populations. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6238, pp. 244–253. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15844-5_25
Lehre, P.K., Qin, X.: Self-adaptation via Multi-objectivisation: a theoretical study. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1417–1425. ACM, Boston, USA (2022)
Lehre, P.K., Yao, X.: On the impact of mutation-selection balance on the runtime of evolutionary algorithms. IEEE Trans. Evol. Comput. 16(2), 225–241 (2012)
Mühlenbein, H., Paaß, G.: From recombination of genes to the estimation of distributions I. Binary parameters. In: International Conference on Parallel Problem Solving From Nature, pp. 178–187. Springer, Berlin, Germany (1996). https://doi.org/10.1007/3-540-61723-X_982
Ochoa, G.: Error thresholds in genetic algorithms. Evol. Comput. 14(2), 157–182 (2006)
Qin, X., Lehre, P.K.: Self-adaptation via Multi-objectivisation: an empirical study. In: Parallel Problem Solving from Nature - PPSN XVII, pp. 308–323. Springer International Publishing, Cham, Switzerland (2022). https://doi.org/10.1007/978-3-031-14714-2_22
Sudholt, D., Witt, C.: Update Strength in EDAs and ACO: how to avoid genetic drift. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 61–68. ACM, Denver Colorado USA (2016)
Vose, M.D.: A linear algorithm for generating random numbers with a given distribution. IEEE Trans. Softw. Eng. 17(9), 972–975 (1991)
Walker, A.J.: New fast method for generating discrete random numbers with arbitrary frequency distributions. Electron. Lett. 10(8), 127–128 (1974)
Acknowledgments
The authors are grateful to Per Kristian Lehre for fruitful discussions of results presented in this paper and for his help with some of the experiments. A. Eremeev is supported by the Mathematical Center in Akademgorodok under the agreement 075-15-2022-282 with the Ministry of Science and Higher Education of RF.
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Dang, DC., Eremeev, A.V., Qin, X. (2024). Empirical Evaluation of Evolutionary Algorithms with Power-Law Ranking Selection. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 703. Springer, Cham. https://doi.org/10.1007/978-3-031-57808-3_16
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