The Hybrid Genetic Optimisation framework (HYGO) is introduced to meet the pressing need for efficient and unified optimisation frameworks that support both parametric and functional learning in complex engineering problems. Evolutionary algorithms are widely employed as derivative-free global optimisation methods but often suffer from slow convergence rates, especially during late-stage learning. HYGO integrates the global exploration capabilities of evolutionary algorithms with accelerated local search for robust solution refinement. The key enabler is a two-stage strategy that balances exploration and exploitation. For parametric problems, HYGO alternates between genetic algorithm and targeted improvement through a degeneracy-proof Dowhill Simplex Method (DSM). For function optimisation tasks, HYGO rotates between genetic programming and DSM. Validation is performed on (a) parametric optimisation benchmarks, where HYGO demonstrates faster and more robust convergence than standard genetic algorithms, and (b) function optimisation tasks, including control of a damped Landau oscillator. Practical relevance is showcased through aerodynamic drag reduction of an Ahmed body via Reynolds-Averaged Navier-Stokes simulations, achieving consistently interpretable results and reductions exceeding 20% by controlled jet injection in the back of the body for flow reattachment and separation bubble reduction. Overall, HYGO emerges as a versatile hybrid optimisation framework suitable for a broad spectrum of engineering and scientific problems involving parametric and functional learning.

翻译：为满足复杂工程问题中对高效统一优化框架的迫切需求，本文提出了混合遗传优化框架（HYGO），该框架同时支持参数学习与函数学习。进化算法作为无导数的全局优化方法已被广泛应用，但往往收敛速度较慢，尤其在后期学习阶段。HYGO将进化算法的全局探索能力与加速局部搜索相结合，以实现鲁棒的解精细化。其关键机制在于采用两阶段策略平衡探索与利用：针对参数优化问题，HYGO在遗传算法与具备退化防护能力的Dowhill单纯形法（DSM）之间交替执行；针对函数优化任务，则在遗传规划与DSM之间轮转。验证工作包括：（a）在参数优化基准测试中，HYGO展现出比标准遗传算法更快、更鲁棒的收敛性能；（b）在函数优化任务中，包括对阻尼Landau振荡器的控制。通过基于雷诺平均Navier-Stokes模拟的Ahmed车身气动减阻案例展示了其实用价值：通过在车身后部实施受控射流以促进流动再附与减小分离泡，HYGO获得了可一致解释的结果，并实现了超过20%的减阻效果。总体而言，HYGO作为一种多功能混合优化框架，适用于涉及参数与函数学习的广泛工程与科学问题。