We propose an evolutionary framework called AutoLR, capable of evolving optimizers for specific tasks. We use the framework to evolve optimizers for a popular
A hybrid method is developed using the evolutionary neural network (EvoNN) as guiding input for the multi-objective optimization using the NSGA-II algorithm to
Multilayer neural networks are trained to solve optimization problems. Genetic algorithms are adopted to "evolve" weights, unveiling new points in the
In this study, we explore the application of genetic algorithms to optimize the architecture of neural networks.
They seek to meta-learn an optimal model initialization in the space of network weights from which the PINN can rapidly converge to a solution that minimizes the physics-informed loss of new tasks. There is huge scope for future research and development into using EAs to jointly meta-learn model initialization, neural architecture, loss function (hyperparameters), and even the gradient-based optimizer to drastically improve convergence on a set of PINN tasks. For example, an EA can jointly optimize the neural architecture and network weights for a PINN such that the meta-learned PINN model utilizes physics compliance during prediction of a new scenario to enhance generalization across diverse tasks with varying initial conditions, PDE parameters, and geometries. There is significant potential in exploring evolutionary computation methods to develop meta-learnable optimizers, loss functions, neural architectures, and initializations tailored to diverse physics-informed learning tasks, thereby presenting opportunities to enhance the generalizability of PINN models.
# 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..
*et al.* A novel neural network model with distributed evolutionary approach for big data classification. # A novel neural network model with distributed evolutionary approach for big data classification. introduces a numerical computing technique using artificial neural networks optimized with particle swarm optimization and active-set algorithms to solve the nonlinear corneal shape model11."). In this study, a distributed processing framework for GA-evolved neural network classifier has been explored, as well as the effectiveness of this framework in big data classification problems. A distributed GA Architecture is employed to train the neural networks to improve the model’s effectiveness in handling large datasets. Table 2 depicts the comparison of accuracy values obtained for GA-based ANN, which works in a normal mode, and the proposed distributed evolutionary neural network. A distributed GA model was adopted to train the neural network. Application of artificial neural network-genetic algorithm (ANN-GA) to correlation of density in nanofluids. A novel neural network model with distributed evolutionary approach for big data classification.
#### Stack Exchange Network. # Why aren't Genetic Algorithms used for optimizing neural networks? Furthermore, training Neural Networks (especially deep ones) is hard and has many issues (non-convex cost functions - local minima, vanishing and exploding gradients etc.). Training Neural Networks (NNs) with Genetic Algorithms (GAs) is not only feasible, there are some niche areas where the performance is good enough to be used frequently. Genetic algorithms and other global searches for optimal parameters are robust in ways that gradient-based algorithms are not. In practice on a large multi-layer network, gradient methods are likely orders of magnitude faster than GA searches such as NEAT for finding network parameters. Still, their research uses backpropagation to train networks, but they use genetic algorithms to find a good architecture. There is another research for network architecture search, but they use bayesian optimization instead of genetic algorithms. ##### Stack Exchange Network.