In the dynamic landscape of real-world problem-solving, the challenges have reached unprecedented levels of complexity. Addressing these intricate issues demands intelligent models and algorithms that can quickly sift through huge amounts of data to find the best solutions.
However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by fusion the skills of different methodologies. By synergizing the strengths of various approaches, researchers integrate ideas and methodologies to explore the potentialities of diverse approaches while compensating for their weaknesses. This fusion of complementary algorithms enables the exploitation of their strengths, ultimately leading to improved overall performance.
Hybrid algorithms can leverage optimization capabilities to guide the learning process and enhance the accuracy and efficiency of decision-making. This integration enables the algorithm to use explicit mathematical optimization techniques and data-driven learning capabilities, leading to more effective and efficient decision-making.
The work (https://link.springer.com/article/10.1007/s10994-023-06467-x) makes a significant contribution by systematically identifying and analysing the existing knowledge on hybrid algorithms that combine optimization and machine learning to solve real-world problems. This study sheds light on the strengths and weaknesses of these algorithms while exploring the opportunities and threats they present. Moreover, it fosters an understanding of the characteristics of these algorithms, serving as a valuable source of inspiration for future research endeavours.
For further details see:
Azevedo, B.F., Rocha, A.M.A.C. & Pereira, A.I. Hybrid approaches to optimization and machine learning methods: a systematic literature review. Machine Learning (2024). https://doi.org/10.1007/s10994-023-06467-x
For further information, please contact us:
Beatriz Flamia Azevedo: beatrizflamia@ipb.pt
Ana I. Pereira: apereira@ipb.p