# Comparative analysis of various Evolutionary Algorithms: Past three decades

## DOI:

https://doi.org/10.4108/eetsis.4356## Keywords:

optimization, Evolutionary algorithms, Genetic Algorithms, Trend Analysis of Genetic Algorithms## Abstract

INTRODUCTION: The Evolutionary algorithms created back in 1953, have gone through various phases of development over the years. It has been put to use to solve various problems in different domains including complex problems such as the infamous problem of Travelling Salesperson (TSP).

OBJECTIVES: The main objective of this research is to find out the advancements in Evolutionary algorithms and to check whether it is still relevant in 2023.

METHODS: To give an overview of the related concepts, subdomains, pros, and cons, the historical and recent developments are discussed and critiqued to provide insights into the results and a better conception of the trends in the domain.

RESULTS: For a better perception of the development of evolutionary algorithms over the years, decade-wise trend analysis has been done for the past three decades.

CONCLUSION: Scope of research in the domain is ever expanding and to name a few EAs for Data mining, Hybrid EAs are still under development.

## References

A First Course in Artificial Intelligence, Deepak Kheemani (2013).

Introduction to Evolutionary Algorithms, Xinjie Yu, Mitsuo Gen.

An overview of evolutionary algorithms: practical issues and common pitfalls, Darrell Whitney

Genetic Programming: A Paradigm For Genetically Breeding Computer Population of Computer Programs to Solve Problems, J.Koza, MIT Press, Cambridge, MA (1992) [5] Katoch, S., Chauhan, S.S. and Kumar, V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80, 8091–8126 (2021). https://doi.org/10.1007/s11042- 020-10139-6

Forrest, S., 1996. Genetic algorithms. ACM Computing Surveys (CSUR), 28(1), pp.77-80.

Kumar, A. (2013). Encoding schemes in genetic algorithm. International Journal of Advanced Research in IT and Engineering, 2(3), 1-7.

Lee, S., 2002. Genetic algorithms. Orthogonal arrays.

Kora, P. and Yadlapalli, P., 2017. Crossover operators in genetic algorithms: A review. International Journal of Computer Applications, 162(10).

Spears, W.M., 1993. Crossover or mutation?. In Foundations of genetic algorithms (Vol. 2, pp. 221-237). Elsevier.

Koza, J.R. and Poli, R., 2005. Genetic programming. In Search methodologies (pp. 127-164). Springer, Boston, MA.[12] Beyer, H.G., 2001. The theory of evolution strategies. Springer Science Business Media.

Xin Yao, Yong Liu and Guangming Lin, ”Evolutionary programming made faster,” in IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 82-102, July 1999, doi: 10.1109/4235.771163.

Floreano, D., D¨urr, P. and Mattiussi, C., 2008. Neuroevolution: from architectures to learning. Evolutionary intelligence, 1(1), pp.47-62.

A. K. Qin, V. L. Huang and P. N. Suganthan, ”Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization,” in IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 398-417, April 2009, doi: 10.1109/TEVC.2008.927706.

Mallipeddi, R., Suganthan, P.N., Pan, Q.K. and Tasgetiren, M.F., 2011. Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied soft computing, 11(2), pp.1679-1696.

B¨ack, T. and Schwefel, H.P., 1993. An overview of evolutionary algorithms for parameter optimization. Evolutionary computation, 1(1), pp.1-23.

Whitley, D., Rana, S., Dzubera, J. and Mathias, K.E., 1996. Evaluating evolutionary algorithms. Artificial intelligence, 85(1-2), pp.245-276.

A. E. Eiben, R. Hinterding and Z. Michalewicz, ”Parameter control in evolutionary algorithms,” in IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 124-141, July 1999, doi: 10.1109/4235.771166.

T.Blickle and L. Thiele, ”A Comparison of Selection Schemes Used in Evolutionary Algorithms,” in Evolutionary Computation, vol. 4, no. 4, pp. 361-394, Dec. 1996, doi: 10.1162/evco.1996.4.4.361.

Goldberg, David E. ”Genetic and evolutionary algorithms come of age.” Communications of the ACM, vol. 37, no. 3, Mar. 1994, pp. 113+. Gale Academic OneFile, link.gale.com/apps/doc/A15061357/AONE?u=anon 5269f96e sid=googleScholarxid=4c409f9a. Accessed 13 Dec. 2022.

Dasgupta, D. and Michalewicz, Z., 1997. Evolutionary algorithms—an overview. Evolutionary Algorithms in Engineering Applications, pp.3-28.

Fonseca, C.M. and Fleming, P.J., 1995. An overview of evolutionary algorithms in multiobjective optimization. Evolutionary computation, 3(1), pp.1-16.

Preux, P. and Talbi, E.G., 1999. Towards hybrid evolutionary algorithms. International transactions in operational research, 6(6), pp.557-570.

Zitzler, E. and Thiele, L., 1998, September. Multiobjective optimization using evolutionary algorithms—a comparative case study. In International conference on parallel problem solving from nature (pp. 292-301). Springer, Berlin, Heidelberg.

Michalewicz, Z., Dasgupta, D., Le Riche, R.G. and Schoenauer, M., 1996. Evolutionary algorithms for constrained engineering problems. Computers Industrial Engineering, 30(4), pp.851-870.

R. Salomon, ”Evolutionary algorithms and gradient search: similarities and differences,” in IEEE Transactions on Evolutionary Computation, vol. 2, no. 2, pp. 45-55, July 1998, doi: 10.1109/4235.728207.

Van Veldhuizen, D.A. and Lamont, G.B., 2000. Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evolutionary computation, 8(2), pp.125-147.

Arifovic, J., 2000. Evolutionary algorithms in macroeconomic models. Macroeconomic Dynamics, 4(3), pp.373-414.

Xin Yao, ”Global optimisation by evolutionary algorithms,” Proceedings of IEEE International Symposium on Parallel Algorithms Architecture Synthesis, 1997, pp. 282-291, doi: 10.1109/AISPAS.1997.581678.

Zitzler, E. and Thiele, L., 1999. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE transactions on Evolutionary Computation, 3(4), pp.257-271.

Whitley, D., 2001. An overview of evolutionary algorithms: practical issues and common pitfalls. Information and software technology, 43(14), pp.817-831. [33] Eiben, A.E., Michalewicz, Z., Schoenauer, M. and Smith, J.E., 2007. Parameter control in evolutionary algorithms. In Parameter setting in evolutionary algorithms (pp. 19-46). Springer, Berlin, Heidelberg.

Streichert, F., 2002. Introduction to evolutionary algorithms. paper to be presented Apr, 4.

Wegener, I., 2001, July. Theoretical aspects of evolutionary algorithms. In International Colloquium on Automata, Languages, and Programming (pp. 64- 78). Springer, Berlin, Heidelberg.

E. Alba and M. Tomassini, ”Parallelism and evolutionary algorithms,” in IEEE Transactions on Evolutionary Computation, vol. 6, no. 5, pp. 443-462, Oct. 2002, doi: 10.1109/TEVC.2002.800880.

Ursem, R.K., 2002, September. Diversity-guided evolutionary algorithms. In International Conference on Parallel Problem Solving from Nature (pp. 462- 471). Springer, Berlin, Heidelberg.

Freitas, A.A., 2002. Data mining and knowledge discovery with evolutionary algorithms. Springer Science Business Media.

Lagaros, N.D., Papadrakakis, M. and Kokossalakis, G., 2002. Structural optimization using evolutionary algorithms. Computers structures, 80(7-8), pp.571- 589.

Rothlauf, F., 2006. Representations for genetic and evolutionary algorithms. In Representations for Genetic and Evolutionary Algorithms (pp. 9-32). Springer, Berlin, Heidelberg.

Grosan, C. and Abraham, A., 2007. Hybrid evolutionary algorithms: methodologies, architectures, and reviews. In Hybrid evolutionary algorithms (pp. 1- 17). Springer, Berlin, Heidelberg.

Freitas, A.A., 2003. A survey of evolutionary algorithms for data mining and knowledge discovery. In Advances in evolutionary computing (pp. 819-845). Springer, Berlin, Heidelberg.

Smit, S.K. and Eiben, A.E., 2009, May. Comparing parameter tuning methods for evolutionary algorithms. In 2009 IEEE congress on evolutionary computation (pp. 399-406). IEEE.

Khare, V., Yao, X. and Deb, K., 2003, April. Performance scaling of multiobjective evolutionary algorithms. In International conference on evolutionary multi-criterion optimization (pp. 376-390). Springer, Berlin, Heidelberg.

Yao, X., Liu, Y., Liang, K.H. and Lin, G., 2003. Fast evolutionary algorithms. In Advances in evolutionary computing (pp. 45-94). Springer, Berlin, Heidelberg.

Vikhar, P.A., 2016, December. Evolutionary algorithms: A critical review and its future prospects. In 2016 International conference on global trends in signal processing, information computing and communication (ICGTSPICC) (pp. 261- 265). IEEE.

Bartz-Beielstein, T., Branke, J., Mehnen, J. and Mersmann, O., 2014. Evolutionary algorithms. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(3), pp.178-195.

Karafotias, G., Hoogendoorn, M. and Eiben, A.E., 2014. Parameter control in ´ evolutionary algorithms: Trends and challenges. IEEE Transactions on Evolutionary Computation, 19(2), pp.167-187.

item Deb, K., 2011. Multi-objective optimisation using evolutionary algorithms: an introduction. In Multi-objective evolutionary optimisation for product design and manufacturing (pp. 3-34). Springer, London.

Slowik, A. and Kwasnicka, H., 2020. Evolutionary algorithms and their applications to engineering problems. Neural Computing and Applications, 32(16), pp.12363-12379.

Mirjalili, S., 2019. Evolutionary algorithms and neural networks. In Studies in computational intelligence (Vol. 780). Berlin/Heidelberg, Germany: Springer.

E. Cantu-Paz and C. Kamath, ”An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems,” in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 35, no. 5, pp. 915-927, Oct. 2005, doi: 10.1109/TSMCB.2005.847740.

Barros, R.C., Basgalupp, M.P., De Carvalho, A.C. and Freitas, A.A., 2011. A survey of evolutionary algorithms for decision-tree induction. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(3), pp.291-312.

Crepinˇsek, M., Liu, S.H. and Mernik, M., 2013. Exploration and exploitation ˇ in evolutionary algorithms: A survey. ACM computing surveys (CSUR), 45(3), pp.1-33. [55] Zhang, G., 2011. Quantum-inspired evolutionary algorithms: a survey and empirical study. Journal of Heuristics, 17(3), pp.303-351.

Cheng, R., He, C., Jin, Y. and Yao, X., 2018. Model-based evolutionary algorithms: a short survey. Complex Intelligent Systems, 4(4), pp.283-292.

Derrac, J., Garc´ıa, S., Hui, S., Suganthan, P.N. and Herrera, F., 2014. Analyzing convergence performance of evolutionary algorithms: A statistical approach. Information Sciences, 289, pp.41-58.

Eiben, A.E. and Smit, S.K., 2011. Evolutionary algorithm parameters and methods to tune them. In Autonomous search (pp. 15-36). Springer, Berlin, Heidelberg.

Biethahn, J. and Nissen, V. eds., 2012. Evolutionary algorithms in management applications. Springer Science Business Media.

Veˇcek, N., Mernik, M. and Crepinˇsek, M., 2014. A chess rating system for ˇ evolutionary algorithms: A new method for the comparison and ranking of evolutionary algorithms. Information Sciences, 277, pp.656-679.

Chugh, T., Singh, M., Nagpal, S., Singh, R. and Vatsa, M., 2017. Transfer learning based evolutionary algorithm for composite face sketch recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 117-125).

Oltean, M. and Grosan, C., 2003. A comparison of several linear genetic programming techniques. Complex Systems, 14(4), pp.285-314.

Brameier, M. and Banzhaf, W., 2001. A comparison of linear genetic programming and neural networks in medical data mining. IEEE Transactions on Evolutionary Computation, 5(1), pp.17-26.

Mirjalili, S. and Lewis, A., 2016. The whale optimization algorithm. Advances in engineering software, 95, pp.51-67.

Price, K.V., 2013. Differential evolution. Handbook of Optimization: From Classical to Modern Approach, pp.187-214.

Slowik, A. and Kwasnicka, H., 2020. Evolutionary algorithms and their applications to engineering problems. Neural Computing and Applications, 32, pp.12363-12379. [67] Deep, K. and Thakur, M., 2007. A new mutation operator for real coded genetic algorithms. Applied mathematics and Computation, 193(1), pp.211-230.

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