Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.
References
[1]
Koza, J.R. (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA.
[2]
Zhong, J.H., Feng, L. and Ong, Y.-S. (2017) Gene Expression Programming: A Survey. IEEE Computational Intelligence Magazine, 12, 54-72. https://doi.org/10.1109/MCI.2017.2708618
[3]
Oltean, M., Groşan, C., Dioşan, L. and Mihăilă, C. (2009) Genetic Programming with Linear Representation: A Survey. International Journal on Artificial Intelligence Tools, 18, 197-238. https://doi.org/10.1142/S0218213009000111
[4]
Angelis, D., Sofos, F. and Karakasidis, T.E. (2023) Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives. Archives of Computational Methods in Engineering, 30, 3845-3865. https://doi.org/10.1007/s11831-023-09922-z
[5]
Bi, Y., Xue, B., Mesejo, P., Cagnoni, S. and Zhang, M. (2023) A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends. IEEE Transactions on Evolutionary Computation, 27, 5-25. https://doi.org/10.1109/TEVC.2022.3220747
[6]
Hosseini, M. and Lim, S. (2022) Gene Expression Programming and Data Mining Methods for Bushfire Susceptibility Mapping in New South Wales, Australia. Natural Hazards, 113, 1349-1365. https://doi.org/10.1007/s11069-022-05350-7
[7]
Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, MI.
[8]
Mitchell, M. (1996) An Introduction to Genetic Algorithm. MIT Press, Cambridge.
[9]
Brabazon, A., O’Neill, M. and McGarraghy, S. (2015) Grammatical Evolution. In: Natural Computing Algorithms, Springer, Berlin, Heidelberg, 357-373. https://doi.org/10.1007/978-3-662-43631-8_19
[10]
O’Neill, M. and Ryan, C. (2001,) Grammatical Evolution. IEEE Transactions on Evolutionary Computation, 5, 349-358. https://doi.org/10.1109/4235.942529
[11]
Bartoli, A., Castelli, M. and Medvet, E. (2020) Weighted Hierarchical Grammatical Evolution. IEEE Transactions on Cybernetics, 50, 476-488. https://doi.org/10.1109/TCYB.2018.2876563
[12]
Ferreira, C. (2001) Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems, 13, 87-129.
[13]
Ferreira, C. (2006) Automatically Defined Functions in Gene Expression Programming. In Nedjah, N., Mourelle, L.d.M. and Abraham, A., Eds., Genetic Systems Programming, Vol. 13, Springer, Berlin, Heidelberg, 21-56. https://doi.org/10.1007/3-540-32498-4_2
[14]
Oltean, M. and Grosan, C. (2003) A Comparison of Several Linear Genetic Programming Techniques. Complex Systems, 14, 285-313. https://wpmedia.wolfram.com/uploads/sites/13/2018/02/14-4-1.pdf
[15]
Miller, J.F. (2020) Cartesian Genetic Programming: Its Status and Future. Genetic Programming and Evolvable Machines, 21, 129-168. https://doi.org/10.1007/s10710-019-09360-6
Rothlauf, F. (2006) Representations for Genetic and Evolutionary Algorithms. In: Representations for Genetic and Evolutionary Algorithms, Springer, Berlin, Heidelberg. https://link.springer.com/content/pdf/10.1007/3-540-32444-5_2.pdf
[18]
Fagan, D., Nicolau, M., O’Neill, M., Galván-López, E., Brabazon, A. and McGarraghy, S. (2010) Investigating Mapping Order in πGE. IEEE Congress on Evolutionary Computation, Barcelona, 18-23 July 2010, 1-7.
[19]
He, P., Johnson, C.G. and Wang, H.F. (2011) Modeling Grammatical Evolution by Automaton. Science China Information Sciences, 54, 2544-2553. https://doi.org/10.1007/s11432-011-4411-8
[20]
He, P., Deng, Z.L., Gao, C.Z., Wang, X.N. and Li, J. (2017) Model Approach to Grammatical Evolution: Deep-Structured Analyzing of Model and Representation. Soft Computing, 21, 5413-5423. https://doi.org/10.1007/s00500-016-2130-1
[21]
Hopcroft, J.E., Motwani, R. and Ullman, J.D. (2008) Introduction to Automata Theory, Languages, and Computation. 3rd Edition, Pearson Education, Inc., San Antonio, TX.
[22]
Aho, A.V., Lam, M.S., Sethi, R. and Ullman, J.D. (2007) Compilers: Principles, Techniques, and Tools. 2nd Edition, Pearson Education, Inc., San Antonio, TX.
[23]
Gupt, K.K., Raja, M.A., Murphy, A., Youssef, A. and Ryan, C. (2022) GELAB—The Cutting Edge of Grammatical Evolution. IEEE Access, 10, 38694-38708. https://doi.org/10.1109/ACCESS.2022.3166115
[24]
Deng, W., He, P. and Qian, J.Y. (2013) Multi-Gene Expression Programming with Depth-First Decoding Principle. Pattern Recognition and Artificial Intelligence, 26, 819-828. (In Chinese)
