Spectrum-Diverse Neuroevolution with Unified Neural Models
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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 problems, first a powerful representation is proposed that unifies most of the neural networks features from the literature into one representation. Secondly, 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 (NEAT) on all of the five classes of problems tested. Ablation tests justifies 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 chromosome size provided by Spectrum Diversity. Thus, this study sheds light on a new representation and diversity preserving mechanism that should impact algorithms and applications to come. To download the code please access the following https://github.com/zweifel/Physis-Shard.
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