Abstract
Memory-based Evolutionary Algorithms in Dynamic Optimization Problems (DOPs) store the best solutions in order to reuse them in future situations. The memorization of the best solutions can be direct (the best individual of the current population is stored) or associative (additional information from the current population is also stored). This paper explores a different type of associative memory to use in Evolutionary Algorithms for DOPs. The memory stores the current best individual and a vector of inhibitions that reflect past errors performed during the evolutionary process. When a change is detected in the environment the best solution is retrieved from memory and the vector of inhibitions associated to this individual is used to create new solutions avoiding the repetition of past errors. This algorithm is called Virtual Loser Genetic Algorithm and was tested in different dynamic environments created using the XOR DOP generator. The results show that the proposed memory scheme significantly enhances the Evolutionary Algorithms in cyclic dynamic environments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report TR AIC-90-001, Naval Research Laboratory (1990)
Grefenstette, J.J.: Genetic algorithms for changing environments. In: Männer, R., Manderick, B. (eds.) Proceedings of PPSN II, pp. 137–144 (1992)
Simões, A., Costa, E.: Variable-Size Memory Evolutionary Algorithm to Deal with Dynamic Environments. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 617–626. Springer, Heidelberg (2007)
Yang, S.: Explicit memory schemes for evolutionary algorithms in dynamic environments. In: Yang, S., Ong, Y.S., Jin, Y. (eds.) Evolutionary Computation in Dynamic and Uncertain Environments. SCI, vol. 51, pp. 3–28. Springer, Heidelberg (2007)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers (2002)
Simões, A., Costa, E.: Prediction in evolutionary algorithms for dynamic environments using markov chains and nonlinear regression. In: Proceedings of GECCO 2009, pp. 883–890. ACM Press (2009)
Trojanowski, K., Michalewicz, Z.: Searching for optima in nonstationary environments. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1999), pp. 1843–1850. IEEE Press (1999)
Yang, S., Yao, X.: Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Computation 5(12), 542–561 (2008)
Yang, S., Richter, H.: Hyper-learning for population-based incremental learning in dynamic environments. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 682–689 (May 2009)
Barlow, G.J., Smith, S.F.: A Memory Enhanced Evolutionary Algorithm for Dynamic Scheduling Problems. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., McCormack, J., O’Neill, M., Romero, J., Rothlauf, F., Squillero, G., Uyar, A.Ş., Yang, S. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 606–615. Springer, Heidelberg (2008)
Sebag, M., Schoenauer, M., Ravisé, C.: Toward civilized evolution: Developing inhibitions. In: Bäck, T. (ed.) Proceedings of the 7th Int. Conference on Genetic Algorithms (ICGA 1997), pp. 291–298. Morgan Kaufmann, San Francisco (1997)
Mitchell, M., Forrest, S., Holland, J.: The royal road for genetic algorithms: fitness landscape and GA performance. In: Varela, F.J., Bourgine, P. (eds.) Proceedings of the First European Conference on Artificial Life, pp. 245–254. MIT Press (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Simões, A., Costa, E. (2012). Virtual Loser Genetic Algorithm for Dynamic Environments. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-29178-4_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29177-7
Online ISBN: 978-3-642-29178-4
eBook Packages: Computer ScienceComputer Science (R0)