In this paper we investigate the convergence properties of a variant of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Our study is based on the recent theoretical foundation that the pure rank-mu update CMA-ES performs the natural gradient descent on the parameter space of Gaussian distributions. We derive a novel variant of the natural gradient method where the parameters of the Gaussian distribution are updated along the natural gradient to improve a newly defined function on the parameter space. We study this algorithm on composites of a monotone function with a convex quadratic function. We prove that our algorithm adapts the covariance matrix so that it becomes proportional to the inverse of the Hessian of the original objective function. We also show the speed of covariance matrix adaptation and the speed of convergence of the parameters. We introduce a stochastic algorithm that approximates the natural gradient with finite samples and present some simulated results to evaluate how precisely the stochastic algorithm approximates the deterministic, ideal one under finite samples and to see how similarly our algorithm and the CMA-ES perform.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Energy costs are an increasingly important issue in real-world scheduling, for both economic and environmental reasons. This paper deals with a variant of the well-known job shop scheduling problem, where we consider a bi-objective optimization of both the weighted tardiness and the energy costs. To this end, we design a hybrid metaheuristic that combines a genetic algorithm with a novel local search method and a linear programming approach. We also propose an efficient procedure for improving the energy cost of a given schedule. In the experimental study we analyse our proposal and compare it with the state of the art and also with a constraint programming approach, obtaining competitive results.

The 119 drones underwent catapult-assisted take-offs and performed aerial formations, the Xinhua News Agency reported on Sunday. The CETC said "swarm intelligence" is regarded as the core of the artificial intelligence of unmanned systems and the future of intelligent unmanned systems. The huge scale of low cost and multi-function UAVs could be used in risky tasks such as emergency communications. CETC engineer Zhao Yanjie said since drones were invented in 1917, intelligent swarms have "changed the rules of the game." In November 2016, the CETC launched 67 drones during the China International Aviation & Aerospace Exhibition in Zhuhai, South China's Guangdong Province, breaking the previous record of 50 drones by the US Navy, CCTV reported.

The OneMax problem is a standard benchmark optimisation problem for a binary search space. Recent work on applying a Bandit-Based Random Mutation Hill-Climbing algorithm to the noisy OneMax Problem showed that it is important to choose a good value for the resampling number to make a careful trade off between taking more samples in order to reduce noise, and taking fewer samples to reduce the total computational cost. This paper extends that observation, by deriving an analytical expression for the running time of the RMHC algorithm with resampling applied to the noisy OneMax problem, and showing both theoretically and empirically that the optimal resampling number increases with the number of dimensions in the search space.

As someone so flummoxed by traffic I wrote a book about it, I have a near-clinical aversion to vehicular congestion. My global default strategy is to simply drive as little as possible, but there are times when I simply must put foot to gas pedal. Like many, I have become increasingly dependent on the Waze app, which, via each drivers' smartphone, turns an inchoate, undifferentiated mass of drivers into something resembling a collective form of networked intelligence. Waze, it occurred to me the other day while stuck in a bit of unexpected congestion (which had been duly flagged by at least 13 "Wazers"), is helping us turn into ants. Every time drivers travel down a path, Waze tracks their speed--information that can then be broadcast to every following driver.

We propose to further extend preliminary investigations of the nature of the problem of evolving practice problems for learners. Using a refinement of a previous simple model of interaction between learners and practice problems, we examine some of its properties and experimentally highlight the role played by the number of values each gene may take in our encoding of practice problems. We then experimentally compare both a traditional - P-CHC - and Pareto-based - P-PHC - variants of coevolutionary algorithms. Comparisons are conducted with respect to the presence of noise in fitness evaluations, the number of values genes may take, and two distinct fitness functions. Each fitness captures an aspect of the nature of learner-problem interaction but one has been shown to induce overspecialization pathologies. We then summarize our findings in terms of guidelines on how to adapt evolutionary algorithms to tackle the task of evolving practice problems.

Building on a survey of previous theories of serendipity and creativity, we advance a sequential model of serendipitous occurrences. We distinguish between serendipity as a service and serendipity in the system itself, clarify the role of invention and discovery, and provide a measure for the serendipity potential of a system. While a system can arguably not be guaranteed to be serendipitous, it can have a high potential for serendipity. Practitioners can use these theoretical tools to evaluate a computational system's potential for unexpected behaviour that may have a beneficial outcome. In addition to a qualitative features of serendipity potential, the model also includes quantitative ratings that can guide development work. We show how the model is used in three case studies of existing and hypothetical systems, in the context of evolutionary computing, automated programming, and (next-generation) recommender systems. From this analysis, we extract recommendations for practitioners working with computational serendipity, and outline future directions for research.

Twenty years ago, Stephen Goldson thought he had beaten the Argentine stem weevil, an invasive insect that was devastating New Zealand's pastures. Goldson, an entomologist, had scoured the South American countryside and come up with an efficient weevil killer: a parasitoid wasp that at first killed up to 90% of the weevils. Now, the weevil has made a comeback and an examination of decades worth of data on its abundance over years has revealed that the weevil has outevolved its parasite, which produces asexually. Now, Goldson and his colleagues are studying weevil DNA to learn the secret of this comeback.

Swarm robotics has experienced a rapid expansion in recent years, primarily fueled by specialized multi-robot systems developed to achieve dedicated collective actions. These specialized platforms are in general designed with swarming considerations at the front and center. Key hardware and software elements required for swarming are often deeply embedded and integrated with the particular system. However, given the noticeable increase in the number of low-cost mobile robots readily available, practitioners and hobbyists may start considering to assemble full-fledged swarms by minimally retrofitting such mobile platforms with a swarm-enabling technology. Here, we report one possible embodiment of such a technology designed to enable the assembly and the study of swarming in a range of general-purpose robotic systems. This is achieved by combining a modular and transferable software toolbox with a hardware suite composed of a collection of low-cost and off-the-shelf components. The developed technology can be ported to a relatively vast range of robotic platforms with minimal changes and high levels of scalability. This swarm-enabling technology has successfully been implemented on two distinct distributed multi-robot systems, a swarm of mobile marine buoys and a team of commercial terrestrial robots. We have tested the effectiveness of both of these distributed robotic systems in performing collective exploration and search scenarios, as well as other classical cooperative behaviors. Experimental results on different swarm behaviors are reported for the two platforms in uncontrolled environments and without any supporting infrastructure. The design of the associated software library allows for a seamless switch to other cooperative behaviors, and also offers the possibility to simulate newly designed collective behaviors prior to their implementation onto the platforms.