Evolutionary Systems

DNA, the famous double helix carrying the genetic instructions used in the growth, development, functioning and reproduction of all living beings, is fundamentally, the critical way of storing the biosphere, and as part of it, all of humanity's information. It is the foundation of life as we scientifically know it. Conventionally, it gathers and encodes instructions for making living things, but it can be encrypted for other purposes and to evolve according to its organic nature evolutionary programming. Scientists and technologists from all kinds of subjects, as they deepen their understanding of its engineering, are adopting the biological DNA to store what seemed unimaginable some years ago, such as books, recordings, GIFs, and even planning things such as an Amazon gift card. In a pioneer experiment, Yaniv Erlich and Dina Zielinski, from the New York Genome Center and Columbia University encoded in a single gram of DNA, one of the first films ever made, Lumiere Brothers "The Arrival of a Train at La Ciotat Station" along with a computer operating system, a photo, a scientific paper, a computer virus, and an Amazon gift card.

With an increasing number of web services, providing an end-to-end Quality of Service (QoS) guarantee in responding to user queries is becoming an important concern. Multiple QoS parameters (e.g., response time, latency, throughput, reliability, availability, success rate) are associated with a service, thereby, service composition with a large number of candidate services is a challenging multi-objective optimization problem. In this paper, we study the multi-constrained multi-objective QoS aware web service composition problem and propose three different approaches to solve the same, one optimal, based on Pareto front construction and two other based on heuristically traversing the solution space. We compare the performance of the heuristics against the optimal, and show the effectiveness of our proposals over other classical approaches for the same problem setting, with experiments on WSC-2009 and ICEBE-2005 datasets.

This paper proposes a tutorial on the Data Clustering technique using the Particle Swarm Optimization approach. Following the work proposed by Merwe et al. here we present an in-deep analysis of the algorithm together with a Matlab implementation and a short tutorial that explains how to modify the proposed implementation and the effect of the parameters of the original algorithm. Moreover, we provide a comparison against the results obtained using the well known K-Means approach. All the source code presented in this paper is publicly available under the GPL-v2 license.

Inspired by recent interests of developing machine learning and data mining algorithms on hypergraphs, we investigate in this paper the semi-supervised learning algorithm of propagating "soft labels" (e.g. probability distributions, class membership scores) over hypergraphs, by means of optimal transportation. Borrowing insights from Wasserstein propagation on graphs [Solomon et al. 2014], we re-formulate the label propagation procedure as a message-passing algorithm, which renders itself naturally to a generalization applicable to hypergraphs through Wasserstein barycenters. Furthermore, in a PAC learning framework, we provide generalization error bounds for propagating one-dimensional distributions on graphs and hypergraphs using 2-Wasserstein distance, by establishing the \textit{algorithmic stability} of the proposed semi-supervised learning algorithm. These theoretical results also shed new lights upon deeper understandings of the Wasserstein propagation on graphs.

We consider a scenario where an agent has multiple available strategies to explore an unknown environment. For each new interaction with the environment, the agent must select which exploration strategy to use. We provide a new strategy-agnostic method that treat the situation as a Multi-Armed Bandits problem where the reward signal is the diversity of effects that each strategy produces. We test the method empirically on a simulated planar robotic arm, and establish that the method is both able discriminate between strategies of dissimilar quality, even when the differences are tenuous, and that the resulting performance is competitive with the best fixed mixture of strategies.

If you're always being criticised for being lazy, it seems you could have a good excuse. A study suggests idleness is an excellent survival strategy – and the sloths among us may represent the next stage in human evolution. Scientists believe they have uncovered a previously overlooked law of natural selection based on'survival of the slacker'. This suggests that laziness can be a good strategy for ensuring the survival of individuals, species and even whole groups of species. Although the research was based on lowly molluscs living on the floor of the Atlantic, the authors believe they may have stumbled on a general principle that could apply to higher animals – including land-dwelling vertebrates.

We present a novel optimization method, named the Combined Optimization Method (COM), for the joint optimization of two or more cost functions. Unlike the conventional joint optimization schemes, which try to find minima in a weighted sum of cost functions, the COM explores search space for common minima shared by all the cost functions. Given a set of multiple cost functions that have qualitatively different distributions of local minima with each other, the proposed method finds the common minima with a high success rate without the help of any metaheuristics. As a demonstration, we apply the COM to the crystal structure prediction in materials science. By introducing the concept of data assimilation, i.e., adopting the theoretical potential energy of the crystal and the crystallinity, which characterizes the agreement with the theoretical and experimental X-ray diffraction patterns, as cost functions, we show that the correct crystal structures of Si diamond, low quartz, and low cristobalite can be predicted with significantly higher success rates than the previous methods.

Over the past several weeks I have been tinkering with an "art generator AI". It's very much a work in progress but people seem to have opinions about this sort of thing so I thought I'd share what I've done, or what it has done, or both, depending on your point of view. I'm sure we've all been part of or heard some version of the nature of art or "is this art?" debate. Algorithmic and AI generated art seems to attract particular questioning. If the art is generated then is it my art or the software's art?

Princeton Univ, $29.95 (288p) ISBN 978-0-691-18351-0 The Greeks thought of everything, including sci-fi tropes such as androids and artificial intelligence, according to this lively study of mythology and technology. Stanford classicist Mayor (The Amazons) surveys myths from ancient Greece (with excursions to India and China) about bio-techne, life crafted by artifice. She finds a trove of them, including those of the bronze warrior-robot Talos, who patrolled Crete, hurling boulders at ships and roasting soldiers alive; statues by the legendary engineer Daedalus, so lifelike that they had to be tethered to stay put; and marvels by the blacksmith god Hephaestus, including automated rolling tripods that served Olympian feasts and talking robot servants to help at his forge. Taking a more organic approach, the witch Medea, after defeating Talos with sweet talk and trickery, invented herbal drugs to reverse aging. Mayor also looks at real-life automata in ancient Alexandria--mechanical beasts and people that moved, vocalized, and dispensed milk to bemused onlookers.

Deep neuroevolution, that is evolutionary policy search methods based on deep neural networks, have recently emerged as a competitor to deep reinforcement learning algorithms due to their better parallelization capabilities. However, these methods still suffer from a far worse sample efficiency. In this paper we investigate whether a mechanism known as "importance mixing" can significantly improve their sample efficiency. We provide a didactic presentation of importance mixing and we explain how it can be extended to reuse more samples. Then, from an empirical comparison based on a simple benchmark, we show that, though it actually provides better sample efficiency, it is still far from the sample efficiency of deep reinforcement learning, though it is more stable.