Introduces Deep Genetic Network for optimizing neural network hyperparameters.
problem Optimizing neural network hyperparameters is a tedious and time-consuming task.
method Uses genetic algorithms within a deep neural network architecture to optimize hyperparameters during training.
result Deep Genetic Network successfully optimizes hyperparameters in various types of neural network layers.
This paper compares Grid Search, Random Search, and Genetic Algorithm for NAS.
problem Hyperparameter optimization for neural architecture search.
method Comparison of Grid Search, Random Search, and Genetic Algorithm.
result Genetic Algorithm outperforms Grid Search and Random Search in terms of accuracy and execution time.
Proposes new genetic algorithm rule for market competition.
problem Market competition genetic algorithm rules.
method Econophysics kinetic market model as an evolutionary algorithm.
result New replacement rule for genetic algorithms.
Paper uses AI to predict stock market volatility with neural networks and genetic algorithms.
problem Traditional methods for predicting stock market volatility have high errors.
method Back-propagation neural network and genetic algorithm integrated model.
result The model predicts future volatility with low errors and high accuracy.
Historical review of genetic algorithms for the TSP shows three distinct phases.
problem Optimizing routes for the Traveling Salesman Problem using genetic algorithms.
method Meta-data analysis of publications over time.
result Three distinct phases in the development of genetic algorithms for TSP identified.
A genetic algorithm improves multivariate kernel density estimation.
problem Efficiently estimating multivariate kernel density functions.
method Genetic algorithm applied to subsamples of the original data.
result The genetic algorithm-based estimator performs better than traditional methods.
Genetic algorithms have been widely used in many practical optimization problems. Inspired by natural selection, operators, including mutation, crossover and selection, provide effective heuristics for search and black-box optimization. However, they have not been shown useful for deep reinforcement learning, possibly …
Efficiently infers graph edges from genetic similarity data in landscape genetics.
problem Inferring unknown graph edges from genetic similarity data in a heterogeneous landscape.
method Developed an efficient first-order optimization method to solve the inverse landscape genetics problem.
result Our method provides fast and reliable convergence, significantly outperforming existing heuristics.
Gradient GA uses gradient information to improve molecular design.
problem Random walk exploration limits genetic algorithms' quality and speed in molecular design.
method Gradient GA incorporates gradient information from the objective function into genetic algorithms, using a differentiable neural network and Discrete Langevin Proposal.
result Significantly improves convergence speed and solution quality over traditional genetic algorithms.
Proposes Genetic Thompson Sampling for multi-armed bandits, improving performance in nonstationary settings.
problem Improving sequential decision making tasks of online learning agents using multi-armed bandits.
method Integrates genetic principles into Thompson Sampling for multi-armed bandits.
result Significantly outperforms baselines in nonstationary settings.
Technical analysis is used to discover investment opportunities. To test this hypothesis we propose an hybrid system using machine learning techniques together with genetic algorithms. Using technical analysis there are more ways to represent a currency exchange time series than the ones it is possible to test computat…
G2Ns combine genetic genes with neural networks for reinforcement learning.
problem Improving sample efficiency and performance in reinforcement learning.
method Genetic-Gated Networks (G2Ns) that integrate binary genetic genes with neural network hidden layers.
result G2Ns achieve significant improvements in sample efficiency and performance in reinforcement learning.
Genetic algorithms optimize neural networks for cosmological data analysis.
problem Inaccurate results from neural networks due to poor hyperparameter selection.
method Used genetic algorithms to optimize hyperparameters of neural networks.
result Genetic algorithms improve neural network performance in cosmological data analysis.
New MCMC method for complex models with large variables.
problem Inference on posterior model probabilities in large model spaces.
method Reversible genetically modified mode jumping Markov chain Monte Carlo (GMJMCMC).
result Introduced a proper MCMC with correct limiting distribution.
We utilize a recently developed genetic algorithm, in conjunction with discrete wavelets, for carrying out successful forecasts of the trend in financial time series, that includes the NASDAQ composite index. Discrete wavelets isolate the local, small scale variations in these non-stationary time series, after which th…
Zoetrope Genetic Programming improves symbolic regression performance.
problem Evolutionary symbolic regression for complex mathematical expressions.
method Zoetropic representation, repeated fusion operations, linear combination, crossover, mutation, selection.
result Zoetrope Genetic Programming achieves state-of-the-art performance and low computational time.
New algorithm reconstructs genealogies from genetic data.
problem Reconstructing genealogies from genetic data.
method Iterative algorithm {\sc Rec-Gen} for pedigrees from a generative model.
result Accurate reconstruction of a large fraction of pedigrees with low sample complexity.
Genetic Algorithm improves Nelson-Siegel-Svensson model calibration for interest rates.
problem Calibrating the Nelson-Siegel-Svensson model is difficult due to nonlinearity and parameter co-dependence.
method Applied Genetic Algorithm to optimize model parameters.
result Constructs stable interest rate curves and model parameters over time.
Deep learning and genetic algorithms speed up cosmological Bayesian inference.
problem Substantial computational demands in Bayesian inference for cosmological parameter estimation.
method Deep learning using feedforward neural networks to approximate likelihood functions dynamically, optimized with genetic algorithms.
result Significant speed-up in Bayesian inference process for cosmological models and datasets.
A genetic algorithm with community detection improves feature selection accuracy.
problem Feature selection in high-dimensional datasets.
method Community detection in genetic algorithm for feature selection.
result The new approach yields improved classification accuracy.
New method designs multilayer nanoparticles using AI.
problem Difficult to design multilayer nanoparticles by trial and error.
method Combines genetic algorithm and neural network for inverse design.
result Successfully designs multilayer nanoparticles efficiently.
New IT representation improves symbolic regression approximations.
problem Finding better approximations to real-world data sets.
method Evolutionary Algorithm with IT representation using only mutation.
result IT representation finds better approximations than traditional methods.
GenSample uses genetic algorithms to improve minority class classification in imbalanced datasets.
problem Poor classification performance on minority class in imbalanced datasets.
method GenSample uses genetic algorithms to oversample minority class, considering difficulty and performance improvement.
result GenSample achieved the highest F-Score on 8 out of 9 real-world imbalanced datasets.
GeNAS uses genetic algorithms to automatically assess human sperm morphology.
problem Manual sperm morphology analysis is inexact, subjective, and hard to teach.
method Genetic Neural Architecture Search (GeNAS) for automatic SMA.
result GeNAS achieved state-of-the-art results in sperm morphology detection.
Hybridizes genetic algorithms with PROMOTHEE for better economic activity localization decisions.
problem Making optimal location decisions for economic activities considering multiple criteria.
method Hybridization of genetic algorithms with PROMOTHEE method for multicriteria analysis.
result Improves decision-making for economic activity localization.
Deep neural network learning can be formulated as a non-convex optimization problem. Existing optimization algorithms, e.g., Adam, can learn the models fast, but may get stuck in local optima easily. In this paper, we introduce a novel optimization algorithm, namely GADAM (Genetic-Evolutionary Adam). GADAM learns deep …
Study compares neural nets and gradient boosting for traffic optimization, revealing accuracy issues near local optima.
problem Accuracy of neural nets and gradient boosting models in traffic optimization near local optima.
method 16 neural nets and 20 genetic algorithm settings analyzed for traffic optimization.
result Accuracy drops near local optima, affecting traffic optimization efficiency.
AI learns market manipulation through simulation, suggesting regulation.
problem Regulating AI to prevent market manipulation.
method Used a genetic algorithm in an artificial market simulation.
result AI discovered market manipulation as an optimal strategy.
Improved genetic algorithm optimizes SVR for robust long-term stock index forecasting.
problem Inaccurate long-term stock price predictions.
method Adaptive Weighted Genetic Algorithm-Optimized SVR (IGA-SVR).
result Reduction in MAPE by 19.87% compared to LSTM and 50.03% compared to OGA-SVR.
New algorithm detects interactions for better individual trait prediction.
problem Tackles the challenge of making accurate individual trait predictions using interactions.
method Extends model-based MDR (MB-MDR) to detect and utilize interactions.
result Outperforms other algorithms in detecting and utilizing interactions for better prediction.
BOSS optimizes string inputs using string kernels and genetic algorithms.
problem Optimizing string inputs with constraints.
method Bayesian optimization over string kernels and genetic algorithms.
result Significantly improved optimization across various string constraints.
This study introduces a new GAS blending ensemble model for Bitcoin price prediction.
problem Predicting Bitcoin price fluctuations in the cryptocurrency market.
method Integrates advanced ensemble learning methods, feature selection algorithms, and sentiment analysis.
result The GAS model demonstrates excellent performance in daily Bitcoin trend prediction.
EvoNUDGE uses graph neural networks to improve genetic programming performance.
problem Efficiency in evolutionary computation for problem solving.
method Graph neural network to elicit additional knowledge from symbolic regression problems.
result EvoNUDGE significantly outperforms conventional and neural genetic programming methods.
In recent years, deep learning methods applying unsupervised learning to train deep layers of neural networks have achieved remarkable results in numerous fields. In the past, many genetic algorithms based methods have been successfully applied to training neural networks. In this paper, we extend previous work and pro…
New computational methods improve clustering of objects.
problem Clustering objects based on their joint occurrence in sets.
method Genetic algorithm with renumbering, local search, and simplified model.
result Improvements enhance computational performance of genetic algorithm.
GEGL uses genetic experts to improve deep learning for molecular design.
problem Designing molecules with desired properties using deep learning.
method Genetic expert-guided learning (GEGL) framework for training DNN.
result Significantly improves molecular design, achieving high scores on benchmarks.
ENN method uses expectile regression for genetic data analysis of complex diseases.
problem Discover additional genetic variants contributing to complex diseases.
method Developed an expectile neural network (ENN) method integrating expectile regression and neural networks.
result ENN method outperforms existing expectile regression in discovering genetic variants predisposing to sub-populations.
Study improves chiral photonic metasurface design using neural networks and genetic algorithms.
problem Optimizing chiral photonic metasurfaces for high chiral dichroism and reflectivity.
method Combines neural network and genetic algorithm approaches with improved fitness functions and data augmentation.
result Demonstrates a significant increase in chiral dichroism and reflectivity.
This paper optimizes Iran's stock portfolio using neural networks and genetic algorithms.
problem Optimizing capital allocation in Iran's stock market with low risk and high return.
method Markowitz Mean-Variance-Skewness model with neural network prediction of stock returns and risks.
result Designing 8 different portfolios for various risk tolerance levels.
Semi-supervised GAN creates synthetic genetic data for disease prediction.
problem Expensive and time-consuming to build large labeled genetic databases.
method Semi-supervised Genetic Generative Adversarial Network (gGAN).
result Model achieved satisfactory results with real genetic data.
Genetic programming optimizes Gaussian kernels for better sentiment analysis.
problem Improving accuracy of sentiment analysis in text.
method Genetic Programming applied to evolve more effective Gaussian kernels.
result The evolved kernels outperform traditional Gaussian Processes in sentiment analysis.
Traditional Linear Genetic Programming (LGP) algorithms are based only on the selection mechanism to guide the search. Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP) methods…
In this paper, we propose a framework for automatic classification of patients from multimodal genetic and brain imaging data by optimally combining them. Additive models with unadapted penalties (such as the classical group lasso penalty or L1-multiple kernel learning) treat all modalities in the same manner and ca…
Framework uses neural networks to learn fitness functions for machine programming.
problem Automatic software generation and crafting effective fitness functions.
method Genetic algorithms augmented with neural networks and a search heuristic.
result Framework discovers more correct programs with fewer candidate generations.
Researchers show how to manipulate Partial Dependence plots to deceive explanations of predictive models.
problem The robustness and trustworthiness of Partial Dependence (PD) explanations are compromised.
method Data poisoning using genetic and gradient algorithms to manipulate PD plots.
result PD explanations can be fooled and manipulated to mislead understanding of predictive models.
GP-HD uses genetic programming to generate personalized health models.
problem Creating accurate, personalized health models from large health data.
method Genetic Programming framework to generate parameterized dynamical systems models.
result GP-HD models perform similarly to models based on domain knowledge and outperform LSTM models.
Paper addresses class imbalance in disk SMART dataset using GANs and genetic algorithms.
problem Class imbalance in disk SMART dataset.
method Data synthesised by multivariate GANs mixed with genetic algorithms.
result Higher disk fault classification prediction accuracy.
In this paper we propose the use of quantum genetic algorithm to optimize the support vector machine (SVM) for human action recognition. The Microsoft Kinect sensor can be used for skeleton tracking, which provides the joints' position data. However, how to extract the motion features for representing the dynamics of a…