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Analog metamaterials (MMs) manipulate their effective medium parameters with difficulty, when their geometric architecture is composed of hybrid compositions. However, genetic algorithms, calculation-analog search algorithms that seek for optimal solutions, can be applied to artificial metamaterial-architecture construction. This paper proposes a novel encoding strategy for metamaterial architecture construction that utilizes multi-parameter extremum-seeking optimization. The binary encoding and decoding of the geometric-layer thickness enables the form’s final dimension to be based on the objective fitness function. The genetic algorithm iteratively optimizes the initial geometrical dielectric-layer thicknesses. Then, combining the initial optimized parameter with the composite-metal multi-loops’ spatial distribution, the final metamaterials were analyzed using numerical analysis software. Based on a co-simulation disposition, the proposed metamaterial exhibits 2.5-GHz broadband features with over 80% physical absorption of spatial waves. The proposed metamaterial simultaneously presents low radar cross-sections, wide polarization insensitivity, and dynamical flexibility. Moreover, the proposed metamaterial, when loaded onto a reference antenna, exhibited good real application capability in radar cross-section reduction for physical passive-equipment invisibility. The numerical-simulation and experimental results of the MM’s absorption and flexibility properties showed good agreement, suggesting the advantage of genetic-algorithm optimization co-simulated with numerical-analysis software for metamaterial architecture construction. It shows good potential application for complicated spatial-geometry formations.