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Propagation Alongside Crossover: An Evolutionary Algorithm for Continuous Optimization and Feature Selection
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DOI：10.1007/s42235-026-00840-x.png)
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Abstract
En 中文
Researchers continuously advance optimization algorithms, recognizing that no meta-heuristic can solve all problem types, as stated by the No Free Lunch theorem. This paper introduces the Propagation alongside Crossover (PAC) algorithm to address continuous optimization challenges. The primary goal of PAC is to structure the algorithmic phases in a manner that achieves a robust balance between exploration and exploitation through appropriately designed mechanisms at each stage. PAC simultaneously leverages the benefits of propagation, crossover, and mutation. Three independent operators are defined to generate new candidate solutions separately, and a novel selection strategy allows individuals produced by each operator, along with members of the current population, to independently enter the next generation. This design preserves population diversity, prevents all individuals from converging toward a single point, and enhances the algorithm’s ability to explore the solution space effectively. A key innovation of PAC is its three-mode propagation mechanism, which comprises local search, linear propagation toward the target point, and tear-drop shaped propagation toward the target point. Tear-drop propagation provides a precise and adaptive search around promising solutions, increasing diversity and preventing entrapment in local optima. The target point is typically set as the global optimum; however, when propagating the global optimum itself, a random point is used as the target to further enhance exploration and escape from local optima. The initial population is generated using chaotic mapping to ensure broad coverage of the search space. PAC was rigorously evaluated on 51 benchmark functions and three engineering problems, considering scalability, convergence, sensitivity, and computational efficiency. Comparative analyses with established optimization algorithms demonstrate PAC’s superior performance, as confirmed by Wilcoxon signed-rank and Friedman statistical tests. Furthermore, PAC was applied as a feature selection method on four diverse datasets, achieving substantial dimensionality reduction while outperforming comparative methods in classification accuracy. These results highlight PAC’s versatility, robustness, and practical effectiveness.
Keywords:
Optimization algorithms
Meta-heuristic algorithms
Continuous optimization
Propagation alongside crossover algorithm
Intrusion detection.
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