@article{14225,
  author = {Marcus Noack and Simon Funke},
  title = {Hybrid Genetic Deflated Newton Method for Global  Optimisation},
  abstract = {Optimisation is a basic principle of nature and has a vast variety of applications in research and industry. There is a plurality of different optimisation procedures which exhibit different strengths and weaknesses in computational efficiency and probability of finding the global optimum. Most methods offer a trade-off between these two aspects. This paper proposes a hybrid genetic deflated Newton (HGDN) method to find local and global optima more efficiently than competing methods. The proposed method is a hybrid algorithm which uses a genetic algorithm to explore the parameter domain for regions containing local minima, and a deflated Newton algorithm to efficiently find their exact locations. In each iteration, identified minima are removed using deflation, so that they can not be found again. The genetic algorithm is adapted as follows: every individual of every generation of offspring searches its adjacent space for optima using Newton\’s method; when found, the optimum is removed by deflation, and the individual returns to its starting position. This procedure is repeated until no more optima can be found. The deflation step ensures that the same optimum is not found twice. In the subsequent genetic step, a new generation of offspring is created, using procreation of the fittest. We demonstrate that the proposed method converges to the global optimum, even for small populations. Furthermore, the numerical results show that the HGDN method can improve the number of necessary function and derivative evaluations by orders of magnitude.},
  year = {2017},
  journal = {Journal of Computational and Applied Mathematics},
  volume = {325},
  pages = {97-112},
  publisher = {Elsevier},
  url = {http://dx.doi.org/10.1016/j.cam.2017.04.047},
  doi = {10.1016/j.cam.2017.04.047},
}