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Automated feature selection in neuroevolution
Evolutionary Intelligence, 2009Feature selection is a task of great importance. Many feature selection methods have been proposed, and can be divided generally into two groups based on their dependence on the learning algorithm/classifier. Recently, a feature selection method that selects features at the same time as it evolves neural networks that use those features as inputs ...
TAN, Maxine Yen Ling +3 more
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Annals of the New York Academy of Sciences, 2011
There is strong evidence that empathy has deep evolutionary, biochemical, and neurological underpinnings. Even the most advanced forms of empathy in humans are built on more basic forms and remain connected to core mechanisms associated with affective communication, social attachment, and parental care.
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There is strong evidence that empathy has deep evolutionary, biochemical, and neurological underpinnings. Even the most advanced forms of empathy in humans are built on more basic forms and remain connected to core mechanisms associated with affective communication, social attachment, and parental care.
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Mutational puissance assisted neuroevolution
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020Artificial Neural Networks (ANN) are often trained using back-propagation, wherein the interconnection weights are determined based on the error gradient. ANNs have also been evolved using neuroevolutionary techniques. The weights in such ANNs are updated randomly by Gaussian mutation.
Divya D. Kulkarni 0001 +1 more
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Neuroevolution in Dynamically Changing Environments
The 2020 Conference on Artificial Life, 2020One goal of the Artificial Life field is to achieve a computational system with a complex richness similar to that of biological life.
Jory Schossau, Arend Hintze
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On the significance of the permutation problem in neuroevolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009In this paper we investigate the impact of the Permutation Problem on a standard Genetic Algorithm evolving neural networks for a difficult control problem. Through the use of Price's equation and an explicit enumeration of permutations within the population we demonstrate that for the given problem and representation the Permutation Problem is not as ...
Haflidason, Stefan, Neville, Richard
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A Wavelet-based Encoding for Neuroevolution
Proceedings of the Genetic and Evolutionary Computation Conference 2016, 2016A new indirect scheme for encoding neural network connection weights as sets of wavelet-domain coefficients is proposed in this paper. It exploits spatial regularities in the weight-space to reduce the genspace dimension by considering the low-frequency wavelet coefficients only.
Sjoerd van Steenkiste +3 more
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2020
NEAT is an algorithm that builds neural networks following an incremental and evolutionary process. It uses a genetic algorithm to evolve networks. In the very early generations, neural networks are very simple, composed of a few nodes and connections. However, complexity is added in each generation.
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NEAT is an algorithm that builds neural networks following an incremental and evolutionary process. It uses a genetic algorithm to evolve networks. In the very early generations, neural networks are very simple, composed of a few nodes and connections. However, complexity is added in each generation.
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The Neuroevolution of Consciousness
World Futures Review, 2015Consciousness is the core of every living being and the key of human evolution. Consciousness is the core of the new paradigm now emerging in every field of science, culture, and spirituality. For centuries, consciousness has been divided from matter like the soul from the physical body.
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Automatic feature selection in neuroevolution
Proceedings of the 7th annual conference on Genetic and evolutionary computation, 2005Feature selection is the process of finding the set of inputs to a machine learning algorithm that will yield the best performance. Developing a way to solve this problem automatically would make current machine learning methods much more useful. Previous efforts to automate feature selection rely on expensive meta-learning or are applicable only when ...
Shimon Whiteson +4 more
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Efficient Neuroevolution for a Quadruped Robot
Lecture Notes in Computer Science, 2012In this research, we investigate whether CoSyNE and CMA-NeuroES algorithms can efficiently optimize neural policy of a quadruped robot. Both of these algorithms are proven to optimize connection weights efficiently on Pole Balancing benchmark. Due to their good results on that benchmark, they are expected to be efficient on other control problems like ...
Shengbo Xu +2 more
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