Evolutionary algorithms for neural networks

medium.com article

Neuroevolution: Evolving Neural Network with Genetic Algorithms

https://medium.com/@roopal.tatiwar20/neuroevolution-evolving-neural-network-w…

# 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..

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en.wikipedia.org article

Neuroevolution - Wikipedia

https://en.wikipedia.org/wiki/Neuroevolution

**Neuroevolution**, or **neuro-evolution**, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules. | Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) by Stanley, D'Ambrosio, Gauci, 2008 | Indirect, non-embryogenic (spatial patterns generated by a Compositional pattern-producing network (CPPN) within a hypercube are interpreted as connectivity patterns in a lower-dimensional space) | Genetic algorithm. | Evolvable Substrate Hypercube-based NeuroEvolution of Augmenting Topologies") (ES-HyperNEAT) by Risi, Stanley 2012 | Indirect, non-embryogenic (spatial patterns generated by a Compositional pattern-producing network (CPPN) within a hypercube are interpreted as connectivity patterns in a lower-dimensional space) | Genetic algorithm. | Evolutionary Acquisition of Neural Topologies (EANT/EANT2) by Kassahun and Sommer, 2005 / Siebel and Sommer, 2007 | Direct and indirect, potentially embryogenic (Common Genetic Encoding) | Evolutionary programming/Evolution strategies | Structure and parameters (separately, complexification) |. | GACNN evolutionary pressure-driven by Di Biasi et al, | Direct | Genetic algorithm, Single-Objective Evolution Strategy, specialized for Convolutional Neural Network | Structure |.

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ai.stackexchange.com article

Why evolutionary training of neural networks is not popular?

https://ai.stackexchange.com/questions/15542/why-evolutionary-training-of-neu…

The main evolutionary algorithm used to train neural networks is Neuro-Evolution of Augmenting Topoloigies, or NEAT. NEAT has seen fairly widespread use.

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arxiv.org article

[PDF] Neuroevolution in Deep Neural Networks - arXiv

https://arxiv.org/pdf/2006.05415

Evolutionary Algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimisation and training of DNNs. Neuroevolution is a term which describes these processes of automated conﬁguration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Prolonged absence of such surveys can lead to a disjointed and fragmented ﬁeld preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for improving perfor-mance and wider application within real-world deep learning problems. [110] the authors propose the LSTM (long short-term memory) neural network which is capable of analysing time series over long time spans in order to make 11 TABLE I SUMMARY OF EA REPRESENTATIONS, GENETIC OPERATORS, PARAMETERS AND ITS VALUES USED IN NEUROEVOLUTION IN THE DESIGN OF DNNS ARCHITECTURES, ALONG WITH THE DATASETS USED IN VARIOUS STUDIES WITH THEIR CORRESPONDING COMPUTATIONAL EFFORT GIVEN IN GPU DAYS.

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ultralytics.com article

A Guide on Evolutionary Algorithms | Ultralytics

https://www.ultralytics.com/blog/what-is-an-evolutionary-algorithm-a-quick-guide

Evolutionary algorithms are optimization techniques that can be used within the realm of machine learning to find the best possible solution or model for a given task. Evolutionary algorithms use an iterative process to find the best solution to a problem. Evolutionary algorithms can help speed up this process by testing many design options and gradually improving them to find the best one. Other than machine learning applications, evolutionary algorithms can also be used in areas like computer vision - the field of AI that enables machines to interpret and analyze visual data such as images and videos. The evolutionary algorithm helps by automatically designing and fine-tuning the CNN’s structure and settings, testing many versions, keeping the best performers, and gradually creating more accurate models. * **Flexibility:** Evolutionary algorithms can be adjusted to work well on many different kinds of problems by changing how solutions are represented and how success is measured.

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