Abstract
Noisy fitness functions occur in many practical applications of evolutionary computation. A standard technique for solving these problems is fitness resampling but this may be inefficient or need a large population, and combined with elitism it may overvalue chromosomes or reduce genetic diversity. We describe a simple new resampling technique called Greedy Average Sampling for steady-state genetic algorithms such as GENITOR. It requires an extra runtime parameter to be tuned, but does not need a large population or assumptions on noise distributions. In experiments on a well-known Inventory Control problem it performed a large number of samples on the best chromosomes yet only a small number on average, and was more effective than four other tested techniques.
S.A. Tarim and B. Hnich are supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. SOBAG-108K027. R. Rossi is supported by Science Foundation Ireland under Grant No. 03/CE3/I405 as part of the Centre for Telecommunications Value-Chain-Driven Research (CTVR) and Grant No. 05/IN/I886.
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References
Aizawa, A., Wah, B.: Scheduling of Genetic Algorithms in a Noisy Environment. Evol. Comput. 2(2), 97–122 (1994)
Arnold, D.V., Beyer, H.-G.: Local Performance of the (1+1)-ES in a Noisy Environment. IEEE Trans. Evolutionary Computation 6(1), 30–41 (2002)
Beielstein, T., Markon, S.: Threshold Selection, Hypothesis Tests, and DOE Methods. In: Congress on Evolutionary Computation, pp. 777–782. IEEE Press, Los Alamitos (2002)
Beyer, H.-G.: Evolutionary Algorithms in Noisy Environments: Theoretical Issues and Guidelines for Practice. Computer Methods in Applied Mechanics and Engineering 186(2–4), 239–267 (2000)
Branke, J., Schmidt, C., Schmeck, H.: Efficient Fitness Estimation in Noisy Environments. In: Genetic and Evolutionary Computation Conference, pp. 243–250. Morgan Kaufmann, San Francisco (2001)
Bui, L.T., Abbass, H.A., Essam, D.: Fitness Inheritance for Noisy Evolutionary Multi-Objective Optimization. In: Genetic and Evolutionary Computation Conference, Washington DC, USA. ACM Press, New York (2005)
de Croon, G., van Dartel, M.F., Postma, E.O.: Evolutionary Learning Outperforms Reinforcement Learning on Non-Markovian Tasks. In: Workshop on Memory and Learning Mechanisms in Autonomous Robots, 8th European Conference on Artificial Life, Canterbury, Kent, UK (2005)
Darwen, P.J.: Computationally Intensive and Noisy Tasks: Coevolutionary Learning and Temporal Difference Learning on Backgammon. Congress on Evolutionary Computation (2000)
Fitzpatrick, J.M., Grefenstette, J.J.: Genetic Algorithms in Noisy Environments. Machine Learning 3, 101–120 (1988)
Goldberg, D.E., Deb, K., Clark, J.H.: Genetic Algorithms, Noise, and the Sizing of Populations. Complex Systems 6, 333–362 (1992)
Gopalakrishnan, G., Minsker, B.S., Goldberg, D.: Optimal Sampling in a Noisy Genetic Algorithm for Risk-Based Remediation Design. In: World Water and Environmental Resources Congress, ASCE (2001)
Hughes, E.J.: Evolutionary Multi-objective Ranking with Uncertainty and Noise. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 329–343. Springer, Heidelberg (2001)
Jin, Y., Branke, J.: Evolutionary Optimization in Uncertain Environments — a Survey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)
Miller, B.L.: Noise, Sampling, and Efficient Genetic Algorithms. PhD thesis, University of Illinois, Urbana-Champaign (1997)
Miller, B.L., Goldberg, D.E.: Optimal Sampling for Genetic Algorithms. Intelligent Engineering Systems Through Artificial Neural Networks 6, 291–298 (1996)
Moriarty, D.E., Schultz, A.C., Grefenstette, J.J.: Evolutionary Algorithms for Reinforcement Learning. Journal of Artificial Intelligence Research 11, 241–276 (1999)
Di Pietro, A., While, L., Barone, L.: Applying Evolutionary Algorithms to Problems With Noisy, Time-Consuming Fitness Functions. In: Congress on Evolutionary Computation, pp. 1254–1261. IEEE, Los Alamitos (2004)
Sano, Y., Kita, H.: Optimization of Noisy Fitness Functions by Means of Genetic Algorithms Using History of Search With Test of Estimation. In: Congress on Evolutionary Computation, pp. 360–365. IEEE, Los Alamitos (2002)
Schmidt, C., Branke, J., Chick, S.E.: Integrating Techniques from Statistical Ranking into Evolutionary Algorithms. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 752–763. Springer, Heidelberg (2006)
Silver, E.A., Pyke, D.F., Peterson, R.: Inventory Management and Production Planning and Scheduling. John-Wiley and Sons, New York (1998)
Smalley, J.B., Minsker, B., Goldberg, D.E.: Risk-Based In Situ Bioremediation Design Using a Noisy Genetic Algorithm. Water Resour. Res. 36(10), 3043–3052 (2000)
Stagge, P.: Averaging Efficiently in the Presence of Noise. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 188–197. Springer, Heidelberg (1998)
Stroud, P.D.: Kalman-Extended Genetic Algorithm for Search in Nonstationary Environments with Noisy Fitness Functions. IEEE Transactions on Evolutionary Computation 5(1), 66–77 (2001)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Then, T.W., Chong, E.K.P.: Genetic Algorithms in Noisy Environments. In: 9th IEEE International Symposium on Intelligent Control, Columbus, Ohio, USA, pp. 225–230 (1994)
Whitley, D., Kauth, J.: GENITOR: A Different Genetic Algorithm. In: Rocky Mountain Conference on Artificial Intelligence, Denver, CO, USA, pp. 118–130 (1988)
Wu, J., Zheng, C., Chien, C.C., Zheng, L.: A Comparative Study of Monte Carlo Simple Genetic Algorithm and Noisy Genetic Algorithm for Cost-Effective Sampling Network Design Under Uncertainty. Advances in Water Resources 29, 899–911 (2006)
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Prestwich, S., Tarim, S.A., Rossi, R., Hnich, B. (2008). A Steady-State Genetic Algorithm with Resampling for Noisy Inventory Control. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_56
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DOI: https://doi.org/10.1007/978-3-540-87700-4_56
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