The paper presents a novel generative Neuro-Evolutionary (NE) method called Hill Climb Modular Assembler Encoding (HCMAE). The target application of the HCMAE is to evolve modular Artificial Neural Networks (ANNs) whose modular structure is known in advance.

Different variants of HCMAE were tested on two well-known ANN benchmarks, i.e. the Two-Spiral problem (feed-forward ANNs), and the Inverted-Pendulum problem (recurrent ANNs), for four different modular neural architectures. Particle Swarm Optimization and Differential Evolution were selected as rivals for HCMAE. Both rival methods were tested in two variants, i.e. a classical one-population variant and cooperative co-evolutionary multi-population variant. The paper presents the proposed method and reports all the experiments.

该论文提出了一种新的生成性神经进化 (NE) 方法，称为 Hill Climb Modular Assembler Encoding (HCMAE)。HCMAE 的目标应用是进化模块化人工神经网络 (ANN)，其模块化结构是预先已知的。

针对四种不同的模块化神经架构，HCMAE 的不同变体在两个众所周知的 ANN 基准测试中进行了测试，即双螺旋问题（前馈 ANN）和倒摆问题（循环 ANN）。粒子群优化和差分进化被选为 HCMAE 的竞争对手。两种竞争方法都在两种变体中进行了测试，即经典的单一种群变体和合作共同进化的多种群变体。本文介绍了所提出的方法并报告了所有实验。