Spectrum-Diverse Neuroevolution With Unified Neural Models

By Danilo Vasconcellos Vargas, Junichi Murata. 2017

44 citations recorded by the source corpus.

Learning algorithms are being increasingly adopted in various applications. However, further expansion will require methods that work more automatically. To enable this level of automation, a more powerful solution representation is needed. However, by increasing the representation complexity, a second problem arises. The search space becomes huge, and therefore, an associated scalable and efficient searching algorithm is also required. To solve both the problems, first a powerful representation is proposed that unifies most of the neural networks features from the literature into one representation. Second, a new diversity preserving method called spectrum diversity is created based on the new concept of chromosome spectrum that creates a spectrum out of the characteristics and frequency of alleles in a chromosome. The combination of spectrum diversity with a unified neuron representation enables the algorithm to either surpass or equal NeuroEvolution of Augmenting Topologies on all of the five classes of problems tested. Ablation tests justify the good results, showing the importance of added new features in the unified neuron representation. Part of the success is attributed to the novelty-focused evolution and good scalability with a chromosome size provided by spectrum diversity. Thus, this paper sheds light on a new representation and diversity preserving mechanism that should impact algorithms and applications to come.

Subfields: Artificial Intelligence

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Citations in the corpus

Evolving Robust Neural Architectures to Defend from Adversarial Attacks (2019, methodology): “To keep a high amount of diversity while searching in a huge search space, the algorithm uses two techniques from neuroevolution called novelty map population and spectrum-diversity [39].”
One Pixel Attack for Fooling Deep Neural Networks (2017, background): “This is critical in the case of generating adversarial images since 1) There are networks that are not differentiable, for instance [5].”
Understanding the One Pixel Attack: Propagation Maps and Locality Analysis (2019, unknown): “…systems that can leverage the non-linearity that arise from complex models by using evolutionary based optimization techniques such as self-organizing classiﬁers (Vargas et al., 2013) and neuroevo-lution with uniﬁed neuron models (Vargas & Murata, 2017) would make for a promising investigation.”