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Evolving Neural Networks through Augmenting Topologies

Evolving Neural Networks through Augmenting Topologies,Kenneth O. Stanley,Risto Miikkulainen

Evolving Neural Networks through Augmenting Topologies   (Citations: 350)
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An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolu- tion of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of differ- ent topologies, (2) protecting structural innovation using speciation, and (3) incremen- tally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an im- portant contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.
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    • ...This neuroevolutionary approach has been applied to control problems (Stanley and Miikkulainen 2002, Hewahi 2005, Gomez et al...

    S. D. Prestwichet al. A neuroevolutionary approach to stochastic inventory control in multi-...

    • ...NEAT (Stanley and Miikkulainen, 2002) is a state of the art evolutionary optimizer for neural networks...

    Alexandre Devertet al. A Study on Scalable Representations for Evolutionary Optimization of G...

    • ...In combination with these diversity mechanisms, three distances are investigated: task specific behavioral distances (Lehman and Stanley, 2008, 2010; Trujillo et al, 2008a, 2008b; Mouret and Doncieux, 2009a, 2009b; Mouret, 2011), Hamming distance on the sensory-motor flow (Doncieux and Mouret, 2010; Gomez, 2009), and genotype-based distances (Goldberg, 1987; Mahfoud, 1997; Stanley and Miikkulainen, 2002)...

    J.-B. Mouretet al. Encouraging Behavioral Diversity in Evolutionary Robotics: An Empirica...

    • ...Yet while neuroevolution has produced successful results in a variety of domains [17, 42, 53, 57, 66], the scale of natural brains remains far beyond reach.,The NEAT method was originally developed to evolve ANNs to solve difficult control and sequential decision tasks and has proven successful in a wide diversity of domains [1, 53, 56, 57, 60, 65].,Complete descriptions of the NEAT method, including experiments confirming the contributions of its components, are available in Stanley and Miikkulainen [53, 55] and Stanley et al. [57].,The first is that evolving complexity requires a mechanism to increase the information content in the genome over generations [53, 55].,In contrast to direct encodings like NEAT [53, 55], genotypic CPPN mutations can have a more global effect on the expressed ANN patterns.,While direct encodings like NEAT [53, 55] can complexify ANNs over generations by adding new nodes and connections through mutation, the indirect HyperNEAT encoding tends to start already with fully connected ANNs [8], which take the entire set of ANN connection weights to represent a partial task solution...

    Sebastian Risiet al. An Enhanced Hypercube-Based Encoding for Evolving the Placement, Densi...

    • ...We compare the evolution of AHHS controllers with that in NEAT [62], we investigate how the performance of the AHHS can be improved, we investigate several internal processes of AHHS controllers, and we analyze the scaling behavior of AHHS controllers to a higher number of carts...

    Heiko Hamannet al. A Hormone-Based Controller for Evaluation-Minimal Evolution in Decentr...

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