Abstract
Excessive dying in nature causes reduction in diversity and leads to extinction of organism. In this work to avoid excessive dying we propose explicit dying strategies as part of Genetic Algorithm. A solution is removed from next generation population in a deterministic way by using dying strategy. Multi-objective Genetic Algorithm (MOGA) takes decision of removal of solution, based on one of these three strategies. Experiments were performed to show impact of dying of solutions and dying strategies on the performance of MOGA. Further to improve performance of MOGA an ensemble of dying strategy is proposed. Ensemble of Dying Strategy based MOGA (EDS-MOGA) has been implemented and its results show that ensemble of dying strategy has given better performance than MOGA with single dying strategy.
This is a preview of subscription content, log in via an institution to check access.
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
Deb, K.: Multi-objective Optimization using Evolutionary Algorithms. John Wiley & Sons, West Sussex (2001)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist nondominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)
Raghuwanshi, M.M., Kakde, O.G.: Multi-parent Recombination operator with Polynomial or Lognormal Distribution for Real Coded Genetic Algorithm. In: 2nd Indian International Conference on Artificial Intelligence (IICAI), pp. 3274–3290 (2005)
Zhang, Q., Zhou, A., Zhao, S.Z., Suganthan, P.N., Liu, W., Tiwari, S.: Multi-objective optimization Test Instances for the CEC 2009 Special Session and Competition, University of Essex, Colchester, UK and Nanyang Technological University, Singapore, Special Session on Performance Assessment of Multi-Objective Optimization Algorithms, Technical Report (2008)
Al-Qunaieer, F.S., Tizhoosh, H.R., Rahnamayan, S.: Opposition Based Computing – A Survey. IEEE Transactions on Evolutionary Computation (2010), 978-1-4244-8126-2/10/ ©2010
Yu, E.L., Suganthan, P.N.: Ensemble of niching algorithms. Inform. Sci. 180(15), 2815–2833 (2000)
Mallipeddi, R., Suganthan, P.N.: Ensemble of constraint handling techniques. IEEE Trans. Evol. Computat. 14(4), 561–579 (2010)
Zhao, S.Z., Suganthan, P.N.: Multi-objective evolutionary algorithm with ensemble of external archives. Int. J. Innovative Comput., Inform. Contr. 6(1), 1713–1726 (2010)
Zhao, S.Z., Suganthan, P.N., Zhang, Q.: Decomposition Based Multiobjective Evolutionary Algorithm with an Ensemble of Neighborhood Sizes. IEEE Trans. on Evolutionary Computation 16(3), 442–446 (2012)
Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1(1) (April 1997)
Tizhoosh, H.R.: Opposition-based learning: A new scheme for machine intelligence. In: Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA and International Conference on Intelligent Agents, Web Technologies and Internet, vol. 1, pp. 695–701 (2005)
Zhoua, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P.N., Zhang, Q.: Multiobjective Evolutionary Algorithms: A Survey of the State-of-the-art. Swarm and Evolutionary Computation 1(1), 32–49 (2011)
Qu, B.-Y., Suganthan, P.N.: Novel Multimodal Problems and Differential Evolution with Ensemble of Restricted Tournament Selection. In: The Proc. IEEE Confrernce on Congress on Evolutionary Computation CEC 2010 (2010)
Qu, B.Y., Suganthan, P.N.: Constrained Multi-Objective Optimization Algorithm with Ensemble of Constraint Handling Methods. School of Electrical and Electronic Engineering Nanyang Technological University, Singapore Available at http://www.ntu.edu.sg/home/epnsugan/index_files/EEAs-EOAs.htm
Mallipeddi, R., Suganthan, P.N.: Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies. Nanyang Technological university, Singapore, Available at http://www.ntu.edu.sg/home/epnsugan/index_files/EEAs-EOAs.htm
Zhang, Q., Liu, W., Li, H.: The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. In: Proc. CEC, pp. 203–208 (2009)
Patel, R., Raghuwanshi, M.M., Malik, L.G.: An Improved Ranking Scheme For Selection Of Parents In Multi-Objective Genetic Algorithm. In: IEEE International Conference on Computer Security and Network Technology (CSNT) 2011, SMVDU Katra, Jammu (J&K), June 3-5, pp. 734–739 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Patel, R., Raghuwanshi, M.M., Malik, L.G. (2013). Ensemble of Dying Strategies Based Multi-objective Genetic Algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-03753-0_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03752-3
Online ISBN: 978-3-319-03753-0
eBook Packages: Computer ScienceComputer Science (R0)