Abstract
In recent years, many-objective optimization has become a popular research topic, after it was noted that algorithms that excelled in solving problems with two objectives were not suitable for problems with more than three objectives. In these more difficult problems, selection pressure towards the Pareto front deteriorates, leading to most solutions becoming non-dominated to each other, which makes selection very difficult. To overcome this, approximate measures, for example epsilon-dominance, relax the competition criteria between solutions and make it easier to eliminate worse solutions that would otherwise be non-dominated. In this paper, epsilon dominance is combined with genetic programming to solve a many-objective optimization problem for the first time. Results show that this combination is promising.
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References
Giagkiozis, I., Fleming, P.J.: Methods for multi-objective optimization: an analysis. Inf. Sci. 293, 338–350 (2015)
Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multiobjective optimization. Evol. Comput. 10(3), 263–283 (2002)
Elkasaby, A., Salah, A., Elfeky, E.: Multiobjective optimization using genetic programming: reducing selection pressure by approximate dominance. In: Proceedings of the 6th International Conference on Operations Research and Enterprise Systems, Porto (2017)
Masood, A., Mei, Y., Chen, G., Zhang, M.: Many-objective genetic programming for job-shop scheduling. In: 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver (2016)
Khare, V., Yao, X., Deb, K.: Performance scaling of multi-objective evolutionary algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds.) EMO 2003. LNCS, vol. 2632, pp. 376–390. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36970-8_27
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. Evol. Comput. 6(2), 182–197 (2002)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. In: Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Zurich (2001)
Corne, D.W., Knowles, J.D., Oates, M.J.: The pareto envelope-based selection algorithm for multiobjective optimization. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 839–848. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45356-3_82
Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-objective Problems, 2nd edn. Springer, New York (2007). https://doi.org/10.1007/978-0-387-36797-2
Li, B., Li, J., Tang, K., Yao, X.: Many-objective evolutionary algorithms: a survey. ACM Comput. Surv. 48(1), 13 (2015)
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. Evol. Comput. 18(4), 577–601 (2013)
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_84
Wang, H., Jiao, L., Yao, X.: Two_Arch2: an improved two-archive algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 19(4), 524–541 (2015)
Bandyopadhyay, S., Chakraborty, R., Maulik, U.: Priority-based ϵ dominance: a new measure in multiobjective optimization. Inf. Sci. 305, 97–109 (2015)
Yu, Y., Ma, H., Zhang, M.: F-MOGP: a novel many-objective evolutionary approach to QoS-aware data intensive web service composition. In: Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015), Sendai (2015)
Cheng, R., Jin, Y., Olhofer, M., Sendhoff, B.: A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 20(5), 773–791 (2016)
Montana, D.J.: Strongly typed genetic programming. Evol. Comput. 3(2), 199–230 (1995)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, 1st edn. A Bradford Book, London (1992)
Hadka, D.: MOEA Framework - A Free and Open Source Java Framework for Multiobjective Optimization. Version 2.8 (2015). http://www.moeaframework.org/
Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization test instances for the CEC 2009 special session and competition. University of Essex, Nanyang Technological University (2009)
Farina, M., Amato, P.: A fuzzy definition of “optimality” for many-criteria optimization problems. Syst. Man Cybern. 34(3), 315–326 (2004)
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Elkasaby, A., Salah, A., Elfeky, E. (2018). Approximate Dominance for Many-Objective Genetic Programming. In: Parlier, G., Liberatore, F., Demange, M. (eds) Operations Research and Enterprise Systems. ICORES 2017. Communications in Computer and Information Science, vol 884. Springer, Cham. https://doi.org/10.1007/978-3-319-94767-9_9
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