Hyper-heuristics (HHs) are heuristics that work with an arbitrary set of search operators or algorithms and combine these algorithms adaptively to achieve a better performance than any of the original heuristics. While HHs lend themselves naturally for distributed deployment, relatively little attention has been paid so far on the design and evaluation of distributed HHs. To our knowledge, our work is the first to present a detailed evaluation and comparison of distributed HHs for real parameter optimization in an island model. Our set of test functions includes well-known benchmark functions and two realistic space-probe trajectory optimization problems. The set of algorithms available to the HHs include several variants of differential evolution, and uniform random search. Our main conclusion is that some of the simplest HHs are surprisingly successful in a distributed environment, and the best HHs we tested provide a robust and stable good performance over a wide range of scenarios and parameters.

Multi-repository software projects are becoming more and more popular, thanks to web-based facilities such as GitHub. Code and process metrics generally assume a single repository must be analyzed, in order to measure the characteristics of a codebase. Thus they are not apt to measure how much relevant information is hosted in multiple repositories contributing to the same codebase. Nor can they feature the characteristics of such a distributed development process. We present a set of novel metrics, based on an original classification of commits, conceived to capture some interesting aspects of a multi-repository development process. We also describe an efficient way to build a data structure that allows to compute these metrics on a set of Git repositories. Interesting outcomes, obtained by applying our metrics on a large sample of projects hosted on GitHub, show the usefulness of our contribution.

Abstract-P2P-based optimization has recently gained interest among distributed function optimization scientists. Several wellknown optimization heuristics have been recently re-designed to exploit the peculiarity of such a distributed environment. The final goal is to perform high quality function optimization by means of inexpensive, fully decentralized machines, which may either be purposely organized in a P2P network, or voluntarily join a running P2P optimization task. In this paper we present the GoDE algorithm (Gossip-based Differential Evolution), which obtains remarkable results on several test functions. We describe in detail the algorithm design and the epidemic mechanism that greatly improves the performance. Experimental results in a simulated environment show how GoDE adapts to network scale and how the epidemic communication protocol can make the algorithm achieve good results even in presence of a high churn rate.Keywords-peer-to-peer, function optimization, differential evolution, heuristic, churn I. PROBLEM AND GOALWhen confronted with a function optimization problem, scientists are presented with a large choice space of algorithms and techniques. Recently, a new dimension has been added to this space: distribution. Optimization tasks could be distributed among a collection of independent machines, with the obvious goal of obtaining a speed-up.Most of the existing research in distributed optimization, however, has been focused on tightly-coupled architectures, such as parallel systems [14] or high-performance clusters, usually managed by a central coordinator [15]. These systems either have strict synchronization requirements or rely completely on a central server, which coordinates the work of clients and acts as a state repository.Quite recently, the possibility to perform optimization tasks in a P2P decentralized network of solvers has been investigated and explored. Our target networking environment consists of independent nodes that are connected via an error-free message passing service. All nodes have an identical role running an identical algorithm. Joining and failing nodes are tolerated automatically via the inherent (or explicit) redundancy of the algorithm design. While such a distributed environment sounds like "no big issue" concerning nodes cooperation, communication and connectivity, it is not trivial at all from the point of view of executing a distributed function optimization task.To simplify the discussion, we briefly define some basic terminology. The system we target is composed by a (potentially large) number of solvers, each of them being a running instance of an optimization algorithm. Each solver iteratively evaluates one or more points of an objective function, with the goal of finding a better objective value (minimum or maximum). Solvers may share information with each other about the progress of the optimization process (solution quality). Subsequent iterations of the local instance of the algorithm may depend not only on the objective values that have been found lo...

Abstract. Decentralized peer-to-peer (P2P) networks (lacking a GRID-style resource management and scheduling infrastructure) are an increasingly important computing platform. So far, little is known about the scaling and reliability of optimization algorithms in P2P environments. In this paper we present empirical results comparing two P2P algorithms for real-valued search spaces in large-scale and unreliable networks. Some interesting, and perhaps counter-intuitive findings are presented: for example, failures in the network can in fact significantly improve performance under some conditions. The two algorithms that are compared are a known distributed particle swarm optimization (PSO) algorithm and a novel P2P branch-and-bound (B&B) algorithm based on interval arithmetic. Although our B&B algorithm is not a black-box heuristic, the PSO algorithm is competitive in certain cases, in particular, in larger networks. Comparing two rather different paradigms for solving the same problem gives a better characterization of the limits and possibilities of optimization in P2P networks.

We