Well File:

Evolutionary Systems

Implementing Particle Swarm Optimization in Tensorflow


Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. It's free, we don't spam, and we never share your email address.

On the Prediction of Evaporation in Arid Climate Using Machine Learning Model


Evaporation calculations are important for the proper management of hydrological resources, such as reservoirs, lakes, and rivers. Data-driven approaches, such as adaptive neuro fuzzy inference, are getting popular in many hydrological fields. This paper investigates the effective implementation of artificial intelligence on the prediction of evaporation for agricultural area. In particular, it presents the adaptive neuro fuzzy inference system (ANFIS) and hybridization of ANFIS with three optimizers, which include the genetic algorithm (GA), firefly algorithm (FFA), and particle swarm optimizer (PSO). Six different measured weather variables are taken for the proposed modelling approach, including the maximum, minimum, and average air temperature, sunshine hours, wind speed, and relative humidity of a given location. Models are separately calibrated with a total of 86 data points over an eight-year period, from 2010 to 2017, at the specified station, located in Arizona, United States of America. Farming lands and humid climates are the reason for choosing this location. Ten statistical indices are calculated to find the best fit model. Comparisons shows that ANFIS and ANFIS–PSO are slightly better than ANFIS–FFA and ANFIS–GA. Though the hybrid ANFIS–PSO (R2= 0.99, VAF = 98.85, RMSE = 9.73, SI = 0.05) is very close to the ANFIS (R2 = 0.99, VAF = 99.04, RMSE = 8.92, SI = 0.05) model, preference can be given to ANFIS, due to its simplicity and easy operation.

Features of a smart city


A smart city is a city that uses technology to provide services and solve city problems. The main goals of a smart city are to improve policy efficiency, reduce waste and inconvenience, improve social and economic quality, and maximize social inclusion. Due to the breadth of technologies that have been implemented under the smart city label, it is difficult to distill a precise definition of a smart city. As the world's population continues to urbanize – by 2050, 66% of the world's population is expected to be urban – there is a global trend toward the creation of smart cities. This tendency not only causes many physical, social, behavioural, economic, and infrastructure issues, but it also creates many opportunities.

A Dynamic Resource Allocation Strategy with Reinforcement Learning for Multimodal Multi-objective Optimization - Machine Intelligence Research


Colored figures are available in the online version at https://link.springer.com/journal/11633 Qian-Long Dang received the B. Eng. He is currently a Ph. His research interests include computational intelligence, swarm intelligence, evolution algorithm, and their applications. Wei Xu received the B. Eng.

C++ Machine Learning Algorithms Inspired by Nature


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).

GitHub - google/evojax


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.

Art in the Age of Machine Learning


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.

Multiobjective Tree-Structured Parzen Estimator

Journal of Artificial Intelligence Research

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.

Optimization of a Thermal Cracking Reactor Using Genetic Algorithm and Water Cycle Algorithm


With the global production of 150 million tons in 2016, ethylene is one of the most significant building blocks in today's chemical industry. Most ethylene is now produced in cracking furnaces by thermal cracking of fossil feedstocks with steam. This process consumes around 8% of the main energy used in the petrochemical industry, making it the single most energy-intensive process in the chemical industry. This paper studies a tubular thermal cracking reactor fed by propane and the molecular mechanism of the reaction within the reactor. After developing the reaction model, the existing issues, such as the reaction, flow, momentum, and energy, were resolved by applying heat to the outer tube wall.

Oscars 2022: Who Got More Winners Right, AI or the Movie Experts?


Every year for the last six years, Unanimous AI has been more accurate than movie critics at predicting Oscar winners. It uses swarm intelligence the power of interactive group decisions enhanced by AI – to transform regular people into expert decision-makers. How did it do this year? Unanimous AI took a group of regular movie fans and created a'hive mind' in which their combined choices are smarter than those of any individual member. "We can take a group of people and turn them into a super organism," founder Louis Rosenberg told IoT World Today's sister publication AI Business.