Abstract
Most of the Evolutionary Algorithms (EA) use a population of candidate solutions to explore the search space following specific rules during an iterative process. These algorithms are designed expecting a good balance between exploration and exploitation during the search process. Besides, the diversity of the population is crucial to properly explore the search space. This article introduces an improved version of the Differential Evolution (DE) algorithm, which employs the moving average (MA) to determine when the population should diversify or intensify by using additional operators. The MA is one of the most used stock market indicators, providing recommendations for selling or buying stocks based on historical data. Here, the MA of the historical fitness and dimension-wise diversity is analyzed to determine if the DE continues operating normally or should diversify or intensify the search using additional operators. An exhaustive benchmark involving 37 optimization functions with different complexity levels confirmed the effectiveness of the proposed approach.
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Notes
- 1.
Some functions can also be found at https://www.sfu.ca/~ssurjano/optimization.html.
- 2.
The convergence curves for the 50 dimensions also have advantages for MADE. However, they were not considered due to the space limitation.
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Navarro, M.A. et al. (2022). Improving the Convergence and Diversity in Differential Evolution Through a Stock Market Criterion. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_11
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