Many real-world multi-objective optimisation problems rely on computationally expensive function evaluations. Multi-objective Bayesian optimisation (BO) can be used to alleviate the computation time to find an approximated set of Pareto optimal solutions. In many real-world problems, a decision-maker has some preferences on the objective functions. One approach to incorporate the preferences in multi-objective BO is to use a scalarising function and build a single surrogate model (mono-surrogate approach) on it. This approach has two major limitations. Firstly, the fitness landscape of the scalarising function and the objective functions may not be similar. Secondly, the approach assumes that the scalarising function distribution is Gaussian, and thus a closed-form expression of an acquisition function e.g., expected improvement can be used. We overcome these limitations by building independent surrogate models (multi-surrogate approach) on each objective function and show that the distribution of the scalarising function is not Gaussian. We approximate the distribution using Generalised value distribution. We present an a-priori multi-surrogate approach to incorporate the desirable objective function values (or reference point) as the preferences of a decision-maker in multi-objective BO. The results and comparison with the existing mono-surrogate approach on benchmark and real-world optimisation problems show the potential of the proposed approach.

As two popular schools of machine learning, online learning and evolutionary computations have become two important driving forces behind real-world decision making engines for applications in biomedicine, economics, and engineering fields. Although there are prior work that utilizes bandits to improve evolutionary algorithms' optimization process, it remains a field of blank on how evolutionary approach can help improve the sequential decision making tasks of online learning agents such as the multi-armed bandits. In this work, we propose the Genetic Thompson Sampling, a bandit algorithm that keeps a population of agents and update them with genetic principles such as elite selection, crossover and mutations. Empirical results in multi-armed bandit simulation environments and a practical epidemic control problem suggest that by incorporating the genetic algorithm into the bandit algorithm, our method significantly outperforms the baselines in nonstationary settings. Lastly, we introduce EvoBandit, a web-based interactive visualization to guide the readers through the entire learning process and perform lightweight evaluations on the fly. We hope to engage researchers into this growing field of research with this investigation.

We propose a novel methodology for estimating the epidemiological parameters of a modified SIRD model (acronym of Susceptible, Infected, Recovered and Deceased individuals) and perform a short-term forecast of SARS-CoV-2 virus spread. We mainly focus on forecasting number of deceased. The procedure was tested on reported data for Poland. For some short-time intervals we performed numerical test investigating stability of parameter estimates in the proposed approach. Numerical experiments confirm the effectiveness of short-term forecasts (up to 2 weeks) and stability of the method. To improve their performance (i.e.

This online course is for students and software developers who want to level up their skills by learning interesting optimization algorithms in C . You will learn some of the most famous AI algorithms by writing it in C from scratch, so we will not use any libraries. We will start with the Genetic Algorithm (GA), continue with Simulated Annealing (SA) and then touch on a less known one: Differential Evolution. Finally, we will look at Ant Colony Optimization (ACO). The Genetic Algorithm is the most famous one in a class called metaheuristics or optimization algorithms. You will learn what optimization algorithms are, when to use them, and then you will solve two problems with the Genetic Algorithm(GA).

The discovery of complex multicellular organism development took millions of years of evolution. The genome of such a multicellular organism guides the development of its body from a single cell, including its control system. Our goal is to imitate this natural process using a single neural cellular automaton (NCA) as a genome for modular robotic agents. In the introduced approach, called Neural Cellular Robot Substrate (NCRS), a single NCA guides the growth of a robot and the cellular activity which controls the robot during deployment. We also introduce three benchmark environments, which test the ability of the approach to grow different robot morphologies. In this paper, NCRSs are trained with covariance matrix adaptation evolution strategy (CMA-ES), and covariance matrix adaptation MAP-Elites (CMA-ME) for quality diversity, which we show leads to more diverse robot morphologies with higher fitness scores. While the NCRS can solve the easier tasks from our benchmark environments, the success rate reduces when the difficulty of the task increases. We discuss directions for future work that may facilitate the use of the NCRS approach for more complex domains.

Evolution Strategies (ES) has been applied to optimization problems for a long period of time. A straightforward implementation of ES can be iteratively perturbing parameters in a pool and keeping those that are most fitting, which is simple yet inefficient. As a consequence, applying such a straightforward algorithm can lead to sub-optimal performance for art creativity. To overcome this generic issue in ES, recent advances have been proposed to improve the performance of ES algorithms. One such improvement is Policy Gradients with Parameter-Based Exploration (PGPE), which estimates gradients in a black-box fashion so the computation of fitness does not have to be differentiable per se.

EvoJAX is a scalable, general purpose, hardware-accelerated neuroevolution toolkit. Built on top of the JAX library, this toolkit enables neuroevolution algorithms to work with neural networks running in parallel across multiple TPU/GPUs. EvoJAX achieves very high performance by implementing the evolution algorithm, neural network and task all in NumPy, which is compiled just-in-time to run on accelerators. This repo also includes several extensible examples of EvoJAX for a wide range of tasks, including supervised learning, reinforcement learning and generative art, demonstrating how EvoJAX can run your evolution experiments within minutes on a single accelerator, compared to hours or days when using CPUs. EvoJAX is implemented in JAX which needs to be installed first.

An examination of machine learning art and its practice in new media art and music.Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.

Machine learning (ML) has become ubiquitous in scientific disciplines. In some applications, accurate data-driven predictions are all that is required; however, in many others, interpretability and explainability of the model is equally important. Interpretability and explainability can provide justification for decisions, promote scientific discovery and ultimately lead to better control/improvement of models [1, 2]. In a complementary fashion, ML models can provide further insight by conveying their level of uncertainty in predictions [3]. Especially in cases of low risk tolerance this type of insight is crucial for building trust in ML models [4]. Rather than focus on black-box ML methods (e.g., neural networks or Gaussian process regression) combined with post hoc explainability tools, the current work focuses on inherently interpretable methods. Interpretable ML methods can be competitive with black-box ML in terms of accuracy and do not require a separate explainability toolkit [4, 5]. Symbolic regression is one such inherently interpretable form of ML wherein an analytic equation is produced that best models input data.

Practitioners often encounter challenging real-world problems that involve a simultaneous optimization of multiple objectives in a complex search space. To address these problems, we propose a practical multiobjective Bayesian optimization algorithm. It is an extension of the widely used Tree-structured Parzen Estimator (TPE) algorithm, called Multiobjective Tree-structured Parzen Estimator (MOTPE). We demonstrate that MOTPE approximates the Pareto fronts of a variety of benchmark problems and a convolutional neural network design problem better than existing methods through the numerical results. We also investigate how the configuration of MOTPE affects the behavior and the performance of the method and the effectiveness of asynchronous parallelization of the method based on the empirical results.