This repo contains the code for neural network weight optimisation using 4 evolutionary algorithms, namely: Genetic Algorithm; Cultural Algorithm
Besides classical ad-hoc algorithms such as backpropagation, this task can be approached by using evolutionary computation, a highly configurable and effective
Besides classical ad-hoc algorithms such as backpropagation, this task can be approached by using Evolutionary Computation, a highly configurable and effective
**Abstract:** Building upon evolutionary theory, this work proposes a deep neural network optimization framework based on evolutionary algorithms to enhance existing pre-trained models, usually trained by backpropagation (BP). Specifically, we consider a pre-trained model to generate an initial population of deep neural networks (DNNs) using BP with distinct hyper-parameters, and subsequently simulate the evolutionary process of DNNs. Moreover, we enhance the evolutionary process, by developing an adaptive differential evolution (DE) algorithm, SA-SHADE-tri-ensin, which integrates the strengths of two DE algorithms, SADE and SHADE, with trigonometric mutation and sinusoidal change of mutation rate. Compared to existing work (e.g., ensembling, weight averaging and evolution inspired techniques), the proposed method better enhanced existing pre-trained deep neural network models (e.g., ResNet variants) on large-scale ImageNet. Our analysis reveals that DE with an adaptive trigonometric mutation strategy yields improved offspring with higher success rates and the importance of diversity in the parent population.
In this work differential evolution strategies are applied in neural networks with integer weights training. These strategies have been introduced by Storn
I'm trying to implement the algorithm called NEAT (NeuroEvolution of Augmented Topologies), but the description in original public paper missed the method of how to evolve the weights of a network, it says. I know that while training a neural network, usually the backpropagation algorithm is used to correct the weights, but it only works if you have a fixed topology (structure) through generations and you know the answer to the problem. So for example, given two parents selected for reproduction either choose a particular weight in the network and swap the value (for our swaps we produced two offspring and then chose the one with the best fitness to survive in the next generation of the population), or choose a particular neuron in the network and swap all the weights for that neuron to produce two offspring. Our mutation operators operated on a single network and would select a random weight and either:.
# Neural Network Optimization (Part 2) — Evolutionary Strategies | by Mandar Angchekar | Medium. # Neural Network Optimization (Part 2) — Evolutionary Strategies. ## Neural Network Optimization (Part 1) — Differential Evolution Algorithm ### Explained and Implemented in Python. This post talks about Evolutionary Strategies (ES), evaluating its potential in training neural networks against the benchmark of traditional backpropagation methods. Central to this exploration was the adaptation of ES to fine-tune neural networks, with a particular focus on optimizing mutation rates for enhanced performance. The code block below illustrates key operations in an evolutionary strategy for neural network optimization: recombining genetic material from two parents to create offspring, mutating offspring by adding Gaussian noise, and assessing fitness by evaluating the negative loss on training data. The plot displays the trend in average fitness of a neural network optimized using Evolutionary Strategies (ES) over a logarithmic scale of generations. Image 15: Neural Network Optimization (Part 1) — Differential Evolution Algorithm. Image 25: 42.Backpropagation: How Neural Networks Learn.
Iterative changes in the weight domain make the network structure rough at first so as to tune it later. Not only does it help to avoid inadequate weight