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. [1] 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.

In this paper we present a new population based metaheuristic called the fixed set search (FSS). The proposed approach represents a method of adding a learning mechanism to the greedy randomized adaptive search procedure (GRASP). The basic concept of FSS is to avoid focusing on specific high quality solutions but on parts or elements that such solutions have. This is done through fixing a set of elements that exist in such solutions and dedicating computational effort to finding near optimal solutions for the underlying subproblem. The simplicity of implementing the proposed method is illustrated on the traveling salesman problem. Our computational experiments show that the FSS manages to find significantly better solutions than the GRASP it is based on and also the dynamic convexized method.

In order to achieve a good level of autonomy in unmanned helicopters, an accurate replication of vehicle dynamics is required, which is achievable through precise mathematical modeling. This paper aims to identify a parametric state-space system for an unmanned helicopter to a good level of accuracy using Invasive Weed Optimization (IWO) algorithm. The flight data of Align TREX 550 flybarless helicopter is used in the identification process. The rigid-body dynamics of the helicopter is modeled in a state-space form that has 40 parameters, which serve as control variables for the IWO algorithm. The results after 1000 iterations were compared with the traditionally used Prediction Error Minimization (PEM) method and also with Genetic Algorithm (GA), which serve as references. Results show a better level of correlation between the actual and estimated responses of the system identified using IWO to that of PEM and GA.

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.

Convolutional Neural Networks (CNNs) have demonstrated their superiority in image classification, and evolutionary computation (EC) methods have recently been surging to automatically design the architectures of CNNs to save the tedious work of manually designing CNNs. In this paper, a new hybrid differential evolution (DE) algorithm with a newly added crossover operator is proposed to evolve the architectures of CNNs of any lengths, which is named DECNN. There are three new ideas in the proposed DECNN method. Firstly, an existing effective encoding scheme is refined to cater for variable-length CNN architectures; Secondly, the new mutation and crossover operators are developed for variable-length DE to optimise the hyperparameters of CNNs; Finally, the new second crossover is introduced to evolve the depth of the CNN architectures. The proposed algorithm is tested on six widely-used benchmark datasets and the results are compared to 12 state-of-the-art methods, which shows the proposed method is vigorously competitive to the state-of-the-art algorithms. Furthermore, the proposed method is also compared with a method using particle swarm optimisation with a similar encoding strategy named IPPSO, and the proposed DECNN outperforms IPPSO in terms of the accuracy.

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.