#### Towards a more efficient representation of imputation operators in TPOT

Automated Machine Learning encompasses a set of meta-algorithms intended to design and apply machine learning techniques (e.g., model selection, hyperparameter tuning, model assessment, etc.). TPOT, a software for optimizing machine learning pipelines based on genetic programming (GP), is a novel e…

#### Evolutionary algorithms

Genetic Algorithm Genetic Programming text

This manuscript contains an outline of lectures course “Evolutionary Algorithms” read by the author in Omsk State University n.a. F.M.Dostoevsky. The course covers Canonic Genetic Algorithm and various other genetic algorithms as well as evolutionary strategies, genetic programming, tab…

#### Self-adaptation of Genetic Operators Through Genetic Programming Techniques

Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Operators are represented as trees and are evolved using genetic programming (GP) tec…

#### Finite-dimensional Gaussian approximation with linear inequality constraints

Gaussian Process Genetic Programming

Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems. We consider the finite-dimensional Gaussian approach from Maatouk and Bay (2017) which can satisfy inequality conditions everywhere (either…

#### Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition

CIFAR DNN Gaussian Process Genetic Programming MNIST

We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models. We build on previous scalable GP research including stochastic variational inference based on inducing inputs, kernel interpolation, and structure exploiting algebra. The key idea of our method is to use Tensor T…

#### A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation

Gaussian Process Genetic Programming

Gaussian processes (GPs) are flexible distributions over functions that enable high-level assumptions about unknown functions to be encoded in a parsimonious, flexible and general way. Although elegant, the application of GPs is limited by computational and analytical intractabilities that arise wh…

#### Remote Sensing Image Classification with Large Scale Gaussian Processes

Bayes Gaussian Process Genetic Programming image Support Vector Machine

Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and…

#### Ensemble Multi-task Gaussian Process Regression with Multiple Latent Processes

Gaussian Process Genetic Programming

Multi-task/Multi-output learning seeks to exploit correlation among tasks to enhance performance over learning or solving each task independently. In this paper, we investigate this problem in the context of Gaussian Processes (GPs) and propose a new model which learns a mixture of latent processes…

#### GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs

Bayes Gaussian Process Genetic Programming

This work centers on the problem of stochastic filtering for systems that yield complex beliefs. The main contribution is GP-SUM, a filtering algorithm for dynamic systems expressed as Gaussian Processes (GP), that does not rely on linearizations or Gaussian approximations of the belief. The algori…

#### On the Design of LQR Kernels for Efficient Controller Learning

Bayes Gaussian Process Genetic Programming

Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on…

#### Analogical-based Bayesian Optimization

Bayes Gaussian Process Genetic Programming

Some real-world problems revolve to solve the optimization problem max_{xinmathcal{X}}fleft(xright) where fleft(.right) is a black-box function and X might be the set of non-vectorial objects (e.g., distributions) where we can only define a symmetric and non-negative similarity score on it. This se…

#### Geometric Semantic Genetic Programming Algorithm and Slump Prediction

Research on the performance of recycled concrete as building material in the current world is an important subject. Given the complex composition of recycled concrete, conventional methods for forecasting slump scarcely obtain satisfactory results. Based on theory of nonlinear prediction method, we…

#### Slope Stability Analysis with Geometric Semantic Genetic Programming

Genetic programming has been widely used in the engineering field. Compared with the conventional genetic programming and artificial neural network, geometric semantic genetic programming (GSGP) is superior in astringency and computing efficiency. In this paper, GSGP is adopted for the classificati…