This paper distinguishes among three levels of evolution in EANNs, ie the evolution of connection weights, architectures and learning rules.
This paper describes an evolutionary approach to design ANNs. The ANNs designed by the evolutionary process are referred to as evolutionary ANNs (EANNs).
Evolutionary artificial neural networks (EANN's) refer to a special class of artificial neural networks (ANN's) in which evolution is another fundamental form
Neuroevolution is a technique that applies biologically inspired methods to evolve artificial neural networks.
# Modern Artificial Neural Networks: Is Evolution Cleverer? Andreas Bahmer, Daya Gupta, Felix Effenberger; Modern Artificial Neural Networks: Is Evolution Cleverer?. Machine learning tools, particularly artificial neural networks (ANN), have become ubiquitous in many scientific disciplines, and machine learning-based techniques flourish not only because of the expanding computational power and the increasing availability of labeled data sets but also because of the increasingly powerful training algorithms and refined topologies of ANN. Some refined topologies were initially motivated by neuronal network architectures found in the brain, such as convolutional ANN. The unique opportunity to compare large neuronal network topologies, processing, and learning strategies with those that have been developed in state-of-the-art ANN has become a reality. The selection of these modern ANN is prone to be biased (e.g., spiking neural networks are excluded) but may be sufficient for a concise overview. ### Sign in via your Institution. Sign in via your Institution.
# Neuroevolution: Evolving Neural Network with Genetic Algorithms. Neuroevolution is a subfield of artificial intelligence (AI) and machine learning that combines evolutionary algorithms(like Genetic Algorithm) with neural networks. The primary idea behind neuroevolution is to evolve neural network architectures and/or their weights to solve problems or perform specific tasks. Before getting into neuroevolution in detail, let us first overview the concepts of neural networks and genetic algorithm. By marrying biological evolution principles with computational models, neuroevolution introduces a paradigm shift in the way neural networks learn, adapt, and solve complex problems. At its essence, neuroevolution harmonizes two powerful concepts — neural networks and genetic algorithms. Neuroevolution involves the application of genetic algorithms to enhance neural networks. They involve creating a population of neural networks, evaluating their performance on a given task, selecting the best-performing networks to serve as parents, and applying genetic operations (crossover and mutation) to produce a new generation of networks. Using Genetic Algorithms to Optimize Artificial Neural Networks..
[86] K. O. Stanley, Efficient Evolution of Neural Networks Through Complexification, Ph.D. thesis, Department of. Computer Sciences, The
RESOURCES [1 ] A logical calculus of the ideas immanent in neuron activity (McCulloch-Pitts artificial neuron): https://www.cs.cmu.edu/~.