Fusing Swarm Intelligence and Self-Assembly for Optimizing Echo State Networks.

ABSTRACT: Optimizing a neural network's topology is a difficult problem for at least two reasons: the topology space is discrete, and the quality of any given topology must be assessed by assigning many different sets of weights to its connections. These two characteristics tend to cause very "rough." objective functions. Here we demonstrate how self-assembly (SA) and particle swarm optimization (PSO) can be integrated to provide a novel and effective means of concurrently optimizing a neural network's weights and topology. Combining SA and PSO addresses two key challenges. First, it creates a more integrated representation of neural network weights and topology so that we have just a single, continuous search domain that permits "smoother" objective functions. Second, it extends the traditional foc

SUBMITTER: Martin CE 

PROVIDER: S-EPMC4539438 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature