AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.

/pdf/autodock-vina-improving-the-speed-and-accuracy-of-docking-2o77zoffbq.pdf

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony Ant colony optimization exploits a similar mechanism for solving optimization problems From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO The goal of this article is to introduce ant colony optimization and to survey its most notable applications

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Ant Colony Optimization

In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.

Cuckoo Search via Lévy flights

It is my pleasure to welcome you to Amsterdam for the 2013 Genetic and Evolutionary Computation Conference (GECCO-2013). This year GECCO is being held in Europe, and we very much hope you enjoy the city of Amsterdam, as well as the many historical and modern places directly reachable from it. We hope to offer you an appealing combination of research and culture worth experiencing.

Organizing GECCO has been for me both a pleasure and an honor. A pleasure, because I have been involved in it since 1999: now a long trip, with this incredible community of skilled people and close friends. An honor, since I am having the possibility to give back to the community what I have got from GECCO, personally and in my own research. The high quality, the ideas, and the possibility to interact with GECCO's attendees had a profound impact in my students, my postdocs, and my actual research program.

Christian Blum served as the editor-in-chief this year, and did a superb job in maintaining the well-known high quality of this conference. GECCO-2013 accepted 204 full papers for oral presentation out of a total of 570 submitted. This large number of submissions accounts for the world interest in this year's GECCO. But also, it represents an acceptance rate of less than 36%, what tells on the expected quality of the accepted works. I am very thankful to Christian Blum, Leonardo Vanneschi (this year's Proceedings Chair), and all the track chairs for their hard work managing the review, selection, and scheduling process for the scientific papers. Thanks also to Linklings and Sheridan Printing for their assistance with the publication of the proceedings.

One of the highlights of every GECCO is the set of free tutorials and free workshops held during the first two days of the conference. I found these to be incredibly helpful when I was still learning about the field, and even now, to keep my knowledge fresh on specialized new trends. Thanks to Gabriela Ochoa for chairing the tutorials and Mike Preuss for chairing the workshops. Guys, you are great.

In addition to the main program, there are many other parts that make GECCO a special and meaningful experience. Evert Haasdijk and Peter Bosman served as Local Chairs, and were very helpful with planning our visit to Amsterdam. Xavier Llora did an excellent job of publicity with the help of our webmaster and design consultant, Gerardo Valencia. Daniele Loiacono did a great job as the Competitions Chair and, as Student Chair, Emilia Tantar masterfully oversaw the process of awarding student travel grants. I would also like to thank Thomas Bartz-Beielstein and Joern Mehnen for their role as EC in Practice Chairs.

Proceedings of the 15th annual conference on Genetic and evolutionary computation

This work deals with the so-called minimum capacitated dominating set (CAPMDS) problem, which is an NP-Hard combinatorial optimization problem in graphs. In this paper we describe the application of a recently introduced hybrid algorithm known as Construct, Merge, Solve & Adapt (CMSA) to this problem. Moreover, we evaluate the performance of a standalone ILP solver. The results show that both CMSA and the ILP solver outperform current state-of-the-art algorithms from the literature. Moreover, in contrast to the ILP solver, the performance of CMSA does not degrade for the largest problem instances. The experimental evaluation is based on a benchmark dataset containing two different graph topologies and considering graphs with variable and uniform node capacities.

/pdf/application-of-cmsa-to-the-minimum-capacitated-dominating-2552e74fza.pdf

Application of CMSA to the minimum capacitated dominating set problem

Large neighbourhood search algorithms for the founder sequence reconstruction problem

We present a novel approach to fully automated reconstruction of tree structures in noisy 2D images. Unlike in earlier approaches, we explicitly handle crossovers and bifurcation points, and impose geometric constraints while optimizing a global cost function. We use manually annotated retinal scans to evaluate our method and demonstrate that it brings about a very substantial improvement.

https://link.springer.com/content/pdf/10.1007%2F978-3-642-15705-9_36.pdf

Reconstructing geometrically consistent tree structures from noisy images

In this paper we present the application of a recently proposed, general, algorithm for combinatorial optimization to the unbalanced minimum common string partition problem. The algorithm, which is labelled Construct, Merge, Solve & Adapt, works on sub-instances of the tackled problem instances. At each iteration, the incumbent sub-instance is modified by adding solution components found in probabilistically constructed solutions to the tackled problem instance. Moreover, the incumbent sub-instance is solved to optimality (if possible) by means of an integer linear programming solver. Finally, seemingly unuseful solution components are removed from the incumbent sub-instance based on an ageing mechanism. The results obtained for the unbalanced minimum common string partition problem indicate that the proposed algorithm outperforms a greedy approach. Moreover, they show that the algorithm is competitive with CPLEX for problem instances of small and medium size, whereas it outperforms CPLEX for larger problem instances.

Christian Blum

Papers

Proceedings of the 15th annual conference on Genetic and evolutionary computation

Application of CMSA to the minimum capacitated dominating set problem

Large neighbourhood search algorithms for the founder sequence reconstruction problem

Reconstructing geometrically consistent tree structures from noisy images

Construct, Merge, Solve and Adapt: Application to Unbalanced Minimum Common String Partition