An artificial world of barriers and plains scattered with food is used to test the feasibility of using genetic algorithms to optimize hebbian neural networks to perform on problems without apriori knowledge of the problem domain. A formal L-System based genetic alphabet for neural networks, titled Lsys, and a neural network genetic modeling tool titled Wp1hgn are introduced. Lsys and Matrix neural network topology genetic encoding methods are compared across 24 experimental runs. Lsys encoding achieved a mean maximum food count of 3802 +- 197 at generation 1000 across 8 runs with varied parameters, compared to 1388 +- 610 for Matrix encoding, a 2.74x performance advantage with an 8.5-fold improvement in consistency as measured by coefficient of variation (5.2% vs 44.0%). All 8 Lsys populations successfully learned to navigate the environment, while 4 of 8 Matrix populations failed to achieve competitive performance at any point during 1000 generations. When transferred to a novel maze environment, Lsys populations demonstrated immediate robust generalization, achieving a mean maximum food count of 2455 +- 176 compared to 422 +- 212 for Matrix populations, a 5.82x advantage that exceeded the training world performance gap. A MatrixLSG control condition, in which initial populations were generated using Lsys genotypes and then evolved using Matrix operators, demonstrated that the performance advantage of Lsys encoding derives primarily from the genetic algorithm operating on the compressed symbolic Lsys alphabet throughout evolution rather than from initial population structure. Lsys encoding is shown to provide faster convergence, higher peak performance, dramatically greater reliability, and superior generalization to novel environments compared to Matrix encoding across all experimental conditions tested.

翻译：一个由障碍物和平原组成、散布着食物的虚拟世界被用于测试利用遗传算法优化赫布型神经网络以解决无先验问题领域知识的可行性。本文引入了基于L-系统的神经网络形式化基因字母表——Lsys，以及神经网络基因建模工具Wp1hgn。通过24次实验运行，比较了Lsys与矩阵神经网络拓扑基因编码方法。在8次不同参数的运行中，Lsys编码在第1000代时达到平均最大食物计数3802±197，而矩阵编码为1388±610，性能优势达2.74倍，且以变异系数衡量的一致性提升了8.5倍（5.2%对比44.0%）。所有8个Lsys种群均成功学会在环境中导航，而8个矩阵种群中有4个在1000代内始终未能达到竞争性表现。当迁移至新型迷宫环境时，Lsys种群展现出即时鲁棒的泛化能力，平均最大食物计数达2455±176，矩阵种群为422±212，性能优势达5.82倍，这一差距甚至超过了训练环境中的表现差异。对照条件MatrixLSG（初始种群使用Lsys基因型生成后通过矩阵算子进化）表明，Lsys编码的性能优势主要源于遗传算法在整个进化过程中对压缩的符号化Lsys字母表的操作，而非初始种群结构。在所有实验条件下，Lsys编码相比矩阵编码均展现出更快的收敛速度、更高的峰值性能、显著增强的可靠性以及更优的向新环境的泛化能力。