Evaluating Encoding of Neuron Configuration and Position in Neuroevolution of Liquid State Machines

This study explores a novel encoding approach for optimizing Liquid State Machines (LSMs) in a Neuroevolution (NE) context. By leveraging a Genetic Algorithm (GA), two components of LSM design, neuron configurations and neuron positions, are used. Three variants were evaluated to assess their individual contributions: the complete proposal considering both components, one encoding with only neuron configurations, and another focusing on neuron positions. Experiments were conducted on varying complexity synthetic classification tasks, demonstrating that neuron configurations significantly influence performance, while neuron positions alone were less effective. Statistical analysis using the Shapiro-Wilk, Kruskal-Wallis, and Dunn's post hoc tests validated these findings. The results highlight the importance of configuration-driven encodings in LSM optimization and suggest the need for refined evolutionary strategies to exploit positional information better.