出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Artificial Neural Networks (ANNs) have been successfully used in Pattern Recognition tasks.
Evolutionary Spiking Neural Networks (ESNNs) constitute an approach to design thirdgeneration
ANNs (also known as Spiking Neural Networks, SNNs) involving Evolutionary
Algorithms (EAs) to govern some intrinsic aspects of the networks, such as topology,
connections and/or parameters. Concerning the practicality of the networks, a rather simple
standard is commonly used; restricted feed-forward fully-connected network topologies
deprived from more complex connections are usually considered. Notwithstanding, a wider
prospect of configurations in contrast to standard network topologies is available for research.
In this paper, ESNNs are evolved to solve pattern classification tasks, using an EA-based
algorithm known as Grammatical Evolution (GE). Experiments demonstrate competitive results
and a distinctive variety of network designs when compared to a more traditional approach to
design ESNNs.
