Imagine a black box which can help us to decide over an unlimited number of possibilities, with a criterion such that we can find an acceptable solution (both in time and quality) to a problem that we formulate. Genetic Algortithms (GA) are a mathematical model inspired by the famous Charles Darwin's idea of natural selection. The natural selection preserves only the fittest individuals, over the different generations. Imagine a population of 100 rabbits in 1900, if we look the population today, we are going to others rabbits more fast and skillful to find food than their ancestors. In machine learning, one of the uses of genetic algorithms is to pick up the right number of variables in order to create a predictive model.

Artificial Intelligence is a very exciting field of study. It has always seemed like the stuff of science fiction. However, Artificial Intelligence (AI) is becoming more and more prevalent and ingrained in our society. Machine Learning, a sub-field of AI where computers learn how to solve a task by incrementally improving their performance, has become commonplace in a wide variety of industries and applications. Examples of machine learning in business include the well-known filtering of spam emails or product reviews, credit card fraud detection, and even programming barbies to have interactive conversations.

We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4 C root mean squared error (RMSE) in a temperature range of 24– 90 C, employing gradient boosting with decision trees. The RMSE is 3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80 C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.

In the last decade or so we have seen tremendous progress in Artificial Intelligence (AI). AI is now in the real world, powering applications that have a large practical impact. Most of it is based on modeling, i.e. machine learning of statistical models that make it possible to predict what the right decision might be in future situations. The next step for AI is machine creativity, i.e. tasks where the correct, or even good, solutions are not known, but need to be discovered. Methods for machine creativity have existed for decades. I believe we are now in a similar situation as deep learning was a few years ago: with the million-fold increase in computational power, those methods can now be used to scale up to creativity in real-world tasks. In particular, Evolutionary Computation is in a unique position to take advantage of that power, and become the next deep learning.

This paper presents a semantic brain computer interface (BCI) agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for Go learning and prediction applications. Additionally, we also establish an Open Go Darkforest (OGD) cloud platform with Facebook AI research (FAIR) open source Darkforest and ELF OpenGo AI bots. The Japanese robot Palro will simultaneously predict the move advantage in the board game Go to the Go players for reference or learning. The proposed semantic BCI agent operates efficiently by the human-based BCI data from their brain waves and machine-based game data from the prediction of the OGD cloud platform for optimizing the parameters between humans and machines. Experimental results show that the proposed human and smart machine co-learning mechanism performs favorably. We hope to provide students with a better online learning environment, combining different kinds of handheld devices, robots, or computer equipment, to achieve a desired and intellectual learning goal in the future.

Physicists speak of four fundamental forces that govern the interactions among the bits of matter that make up our universe. The strongest of these four forces, aptly known as the Strong Force, is so powerful that it can keep an atom's positively charged protons from ripping the atom's nucleus apart as their mutually repellent positive charges push them in opposite directions. The second fundamental force, electromagnetism, is 137 times weaker than the strong force, but its ability to cause bits of matter with opposing electrical charges to attract each other, and to cause bits of matter with like charges to avoid each other, is what gives unique three-dimensional structure to atoms, molecules, and even the proteins that form the building blocks of our body's cells. At only one-millionth the strength of the strong force, the third fundamental force--the so-called weak force--changes quarks from one bizarre "flavor" to another and gives rise to nuclear fusion reactions. The weak force deserves a better name: It's actually the fourth force--gravity--that's the weakling of the bunch.

Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization.

Unless you've just woken up from a several-year cryostasis, you're probably aware of the recent resurgence of machine learning and AI. This is yet another cycle of enthusiasm (historically interspersed with so-called Winters), and this one is fueled mostly by interest in recommendation systems and the advances -- in algorithmics and supporting hardware -- of neural networks for machine vision and other purposes. It is therefore worthwhile to also consider other machine learning approaches, not as significantly blessed by the current hype. So, let's talk about evolution. The generic proper term for any sort of heuristic approach that is inspired and/or mimics the process of evolution is Evolutionary Algorithms.

Several test function suites are being used for numerical benchmarking of multiobjective optimization algorithms. While they have some desirable properties, like well-understood Pareto sets and Pareto fronts of various shapes, most of the currently used functions possess characteristics that are arguably under-represented in real-world problems. They mainly stem from the easier construction of such functions and result in improbable properties such as separability, optima located exactly at the boundary constraints, and the existence of variables that solely control the distance between a solution and the Pareto front. Here, we propose an alternative way to constructing multiobjective problems-by combining existing single-objective problems from the literature. We describe in particular the bbob-biobj test suite with 55 bi-objective functions in continuous domain, and its extended version with 92 bi-objective functions (bbob-biobj-ext). Both test suites have been implemented in the COCO platform for black-box optimization benchmarking. Finally, we recommend a general procedure for creating test suites for an arbitrary number of objectives. Besides providing the formal function definitions and presenting their (known) properties, this paper also aims at giving the rationale behind our approach in terms of groups of functions with similar properties, objective space normalization, and problem instances. The latter allows us to easily compare the performance of deterministic and stochastic solvers, which is an often overlooked issue in benchmarking.

Programming a robot to deal with open-ended tasks remains a challenge, in particular if the robot has to manipulate objects. Launching, grasping, pushing or any other object interaction can be simulated but the corresponding models are not reversible and the robot behavior thus cannot be directly deduced. These behaviors are hard to learn without a demonstration as the search space is large and the reward sparse. We propose a method to autonomously generate a diverse repertoire of simple object interaction behaviors in simulation. Our goal is to bootstrap a robot learning and development process with limited informations about what the robot has to achieve and how. This repertoire can be exploited to solve different tasks in reality thanks to a proposed adaptation method or could be used as a training set for data-hungry algorithms. The proposed approach relies on the definition of a goal space and generates a repertoire of trajectories to reach attainable goals, thus allowing the robot to control this goal space. The repertoire is built with an off-the-shelf simulation thanks to a quality diversity algorithm. The result is a set of solutions tested in simulation only. It may result in two different problems: (1) as the repertoire is discrete and finite, it may not contain the trajectory to deal with a given situation or (2) some trajectories may lead to a behavior in reality that differs from simulation because of a reality gap. We propose an approach to deal with both issues by using a local linearization between the motion parameters and the observed effects. Furthermore, we present an approach to update the existing solution repertoire with the tests done on the real robot. The approach has been validated on two different experiments on the Baxter robot: a ball launching and a joystick manipulation tasks.