In this way, GAs can accommodate a wide range of problem-solving and optimisation-based applications, where the algorithms scale well in terms of the solution time required as the size and difficulty of the problem grows (Goldberg, 2013). Given their inherent parallelism, where many different possibilities can be explored simultaneously in an efficient manner, GAs thus lend themselves particularly well to computational problems which require adaptive, innovative, and complex solutions (Goldberg & Holland, 1988; Mitchell, 1998). For example, GAs have been employed to optimise stopping patterns for passenger rail transportation (Lin & Ku, 2014), undertake image enhancement and segmentation (Paulinas & Ušinskas, 2015), construct school timetable solutions (Pillay, 2014), and evolve neural networks (David & Greental, 2014). However, while GAs can be successfully utilised to solve increasingly difficult problems across a wide spectrum of areas, clear and robust design principles are needed to ensure the development of competent GAs which can solve hard problems, quickly, reliably and accurately, with a premium on efficiency (Sastry, Goldberg, & Kendall, 2014).
David, O. E., & Greental, I. (2014). Genetic algorithms for evolving deep neural networks. In Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation (pp. 1451-1452). ACM.
Goldberg, D. E. (2013). The design of innovation: Lessons from and for competent genetic algorithms (Vol. 7). Springer Science & Business Media: Dordrecht, Holland.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
Lin, D. Y., & Ku, Y. H. (2014). Using genetic algorithms to optimize stopping patterns for passenger rail transportation. Computer‐Aided Civil and Infrastructure Engineering, 29(4), 264-278.
Mitchell, M. (1998). An introduction to genetic algorithms. MIT Press: Cambridge, Massachusetts.
Paulinas, M., & Ušinskas, A. (2015). A survey of genetic algorithms applications for image enhancement and segmentation. Information Technology and Control, 36(3), 278-284.
Pillay, N. (2014). A survey of school timetabling research. Annals of Operations Research, 218(1), 261-293.
Sastry, K., Goldberg, D. E., & Kendall, G. (2014). Genetic algorithms. In E. K. Burke & G. Kendall (Eds.) Search methodologies (pp. 93-117). Springer US: New York.