Many real-world optimization problems are not naturally homogeneous vectors but composite design objects with heterogeneous parameters: integers, real values, Booleans, categoricals, complex-valued descriptors, and embedding vectors. Standard evolutionary algorithms flatten these into a single chromosome and apply generic operators with rounding and repair, sacrificing representational fidelity. We introduce the Geno-Synthetic Algorithm (GSA), a type-factored coevolutionary framework in which gene families are partitioned by representational type, evolved in parallel with type-native operators, and assembled into executable phenotypes for joint fitness evaluation. GSA is formalized as a typed product-space search procedure with an explicit assembly operator. An open-source reference implementation (gsa-experiments, MIT-licensed) is released. A focused empirical study compares eight GSA variants against five baselines across seven benchmark problems (six synthetic plus the external COCO BBOB-MixInt suite) at budgets from 5,000 to 100,000 evaluations. The headline finding is architectural: GSA is the only method that operates when gene families include complex-valued descriptors or embedding vectors. On smooth synthetic multi-family problems, well-tuned flattened differential evolution remains the strongest baseline; on BBOB-MixInt at 100,000 evaluations, GSA_DIRECT becomes statistically indistinguishable from FLATTENED_DE while FLATTENED_EA drops from second to fifth rank, an asymptotic crossover. Ablations confirm that type-native operators are essential, elite credit dominates ensemble credit, and active assembly outperforms passive concatenation on gated benchmarks. The framework extends naturally to prompt and embedding optimization for large language model systems.

翻译：许多现实世界的优化问题并非天然的同质向量，而是具有异质参数的设计对象：整数、实数值、布尔值、类别变量、复值描述符与嵌入向量。标准进化算法将这些参数压平为单一染色体，并通过舍入与修复操作使用通用算子，从而牺牲了表征保真度。我们提出基因合成算法（GSA），一种类型分解式协同进化框架：基因家族按表征类型划分，并行采用类型原生算子进化，并组装为可执行表型进行联合适应度评估。GSA被形式化为具有显式组装算子的类型化乘积空间搜索过程。我们发布了开源参考实现（gsa-experiments，MIT许可）。通过聚焦实证研究，我们在七个基准问题（六个合成问题及外部COCO BBOB-MixInt套件）上，以5,000至100,000次评估预算，比较了八种GSA变体与五种基线方法。核心结构发现：GSA是唯一可在基因家族包含复值描述符或嵌入向量时运作的方法。在平滑合成多家族问题上，调优的压平差分进化仍是最强基线；在BBOB-MixInt上以100,000次评估时，GSA_DIRECT在统计上与FLATTENED_DE无显著差异，而FLATTENED_EA从第二降至第五位，呈现渐近交叉。消融实验证实：类型原生算子不可或缺，精英信用主导集成信用，且主动组装在门控基准上优于被动拼接。该框架可自然扩展至大语言模型系统的提示词与嵌入优化。