Evolutionary Systems

Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem.

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.

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.

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.

Real-world problems often involve the optimisation of multiple conflicting objectives. These problems, referred to as multi-objective optimisation problems, are especially challenging when more than three objectives are considered simultaneously. This paper proposes an algorithm to address this class of problems. The proposed algorithm is an evolutionary algorithm based on an evolution strategy framework, and more specifically, on the Covariance Matrix Adaptation Pareto Archived Evolution Strategy (CMA-PAES). A novel selection mechanism is introduced and integrated within the framework.

This introduction is intended for everyone, specially those who are interested in learning about something new. No pre-existing knowledge of the subject or any scientific background is expected.

In multi-objective Bayesian optimization and surrogate-based evolutionary algorithms, Expected HyperVolume Improvement (EHVI) is widely used as the acquisition function to guide the search approaching the Pareto front. This paper focuses on the exact calculation of EHVI given a nondominated set, for which the existing exact algorithms are complex and can be inefficient for problems with more than three objectives. Integrating with different decomposition algorithms, we propose a new method for calculating the integral in each decomposed high-dimensional box in constant time. We develop three new exact EHVI calculation algorithms based on three region decomposition methods. The first grid-based algorithm has a complexity of $O(m\cdot n^m)$ with $n$ denoting the size of the nondominated set and $m$ the number of objectives. The Walking Fish Group (WFG)-based algorithm has a worst-case complexity of $O(m\cdot 2^n)$ but has a better average performance. These two can be applied for problems with any $m$. The third CLM-based algorithm is only for $m=3$ and asymptotically optimal with complexity $\Theta(n\log{n})$. Performance comparison results show that all our three algorithms are at least twice faster than the state-of-the-art algorithms with the same decomposition methods. When $m>3$, our WFG-based algorithm can be over $10^2$ faster than the corresponding existing algorithms. Our algorithm is demonstrated in an example involving efficient multi-objective material design with Bayesian optimization.

Students are usually allocated tosupervisors for their projects by means of a centralized human decision maker or by means of interactions between students and staff members. The decision makers have to take into consideration the preferences of both students and supervisors with respect to the conduct of the project, as well as departmental constraintssuch as minimum and maximum levels of workload (in terms of supervision) for each supervisor. This situation results in an extremely time consuming process, and a suboptimal allocation due to a large and complex search space faced by human decision makers. Automating this process by applying artificial intelligence techniques may enhance the process in terms of satisfaction and performance of students with these individual projects. In this article, we present a genetic algorithm for matching students to supervisors accordingto both students' and supervisors' preferences and the constraints of the department. The rationale behind this problem is matching an appropriate student with a supervisor for the development of an individual project.The problem of matching students to supervisors, or students to projects [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], is a subclass of the wider problem of matching between two sets, one of the most studied fields in computer sciencedue to its applications to a wide range of domains such as the hospital/residents (HR) or the college admission (CA) problem [14, 15, 16]. Particularly, the student-supervisor allocation problem solved in this article can be considered as an instance of the CA problem with lower and upper quotas, where the colleges are the supervisors, both colleges and students (i.e., supervisors andstudents in our case) have some representation of preferences on each other for the conduct of a project, and the minimum and maximum quotas are the minimum and maximum number of students to be supervised by staff members.