An optimization algorithm guided by a machine learning approach


Extracting knowledge is the multidisciplinary process of identifying novel, significant, potentially useful, and consistent information in data. One of the most interesting techniques in the fields of extracting knowledge and machine learning are the self-organization maps (SOMs). They have the capacity of mapping complex high-dimensional relations onto a reduced lattice preserving the topological organization of the initial data. On the other hand, Evolutionary approaches provide an effective alternative to solve complex optimization problems in different application domains. One important characteristic in the application of evolutionary methods to real-world problems is its high demand for function evaluations before obtaining a satisfying solution. In their operation, evolutionary techniques produce new solutions without extracting useful knowledge from a large number of solutions already generated. The use of acquired knowledge during the evolution process could significantly improve their performance in conducting the search strategy toward promising regions or increasing its convergence properties. This paper introduces an evolutionary optimization algorithm in which knowledge extracted during its operation is employed to guide its search strategy. In the approach, a SOM is used as extracting knowledge technique to identify the promising areas through the reduction of the search space. Therefore, in each generation, the proposed method uses a subset of the complete group of generated solutions seen so-far to train the SOM. Once trained, the neural unit from the SOM lattice that corresponds to the best solution is identified. Then, by using local information of this neural unit an entire population of candidate solutions is produced. With the use of the extracted knowledge, the new approach improves the convergence to difficult high multi-modal optima by using a reduced number of function evaluations. The performance of our approach is compared to several state-of-the-art optimization techniques considering a set of well-known functions and three real-world engineering problems. The results validate that the introduced method reaches the best balance regarding accuracy and computational cost over its counterparts.

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Correspondence to Erik Cuevas.

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Appendix A. List of benchmark functions.

See Table 19.

Table 19 Benchmark functions used in the experimental study

Appendix B.

See Table 20.

Table 20 List of CEC2017 benchmark functions

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Cuevas, E., Galvez, J. An optimization algorithm guided by a machine learning approach. Int. J. Mach. Learn. & Cyber. 10, 2963–2991 (2019).

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  • Metaheuristics
  • Self-organization maps
  • Extracting knowledge
  • Machine learning
  • Hybrid systems