In this paper we compare the performance of Minton’s et al. min-conflicts hill-climbing (MCHC) algorithm [8], Wallace and Freuder’s MCHC algorithm [18], Morris’ breakout algorithm (BA) [9, 10], and modified variants of the BA that we developed on static constraint satisfaction problems and recurrent dynamic constraint satisfaction problems (CSPs & rDCSPs). In this study, our results show that t...

Course timetabling problems are real world constraint optimization problems that are often coped with in educational institutions, such as universities or high schools. In this paper, we present a variety of new operators that can be also applied in evolutionary algorithms for other timetabling problems, such as, exam timetabling. Operators include violation directed mutations, crossovers, and ...

Hill climbing is used to maximize an information theoretic measure of the difference between the actual behavior of a unit and the behavior that would be predicted by a statistician who knew the first order statistics of the inputs but believed them to be independent. This causes the unit to detect higher order correlations among its inputs. Initial simulations are presented, and seem encouragi...

In this paper we introduce two novel methods for performing Bayesian network structure search that make use of Gaussian Process regression. Using a relatively small number of samples from the posterior distribution of Bayesian networks, we are able to find an accurate function approximator based on Gaussian Processes. This allows us to remove our dependency on the data during the search and lea...

In this paper, we close the gap between the simple and straight-forward implementations of top-down hill-climbing that can be found in the literature, and the comparably complex strategies for greedy bottom-up generalization. Our main result is that the simple bottom-up counterpart to the top-down hill-climbing algorithm is unable to learn in domains with comparably dispersed examples. In parti...

In this paper, maximum power point tracking (MPPT) of a photovoltaic (PV) system is performed under partial shading conditions (PSCs) using hill climbing (HC)–artificial electric field algorithm (AEFA) considering DC/DC buck converter. The AEFA inspired by Coulomb’s law electrostatic force and has high speed optimization accuracy. Because the traditional HC method cannot perform global search i...

Virtualization provides several benefits to users in the datacenter in terms of infrastructure cost savings (e.g., capital, power, space, cooling, labor). Examples include highly efficient and available resource, networking, and storage management. As many workloads have moved to virtualized environments, it is critical that vSphere handles scale and performs optimally. However, there are scena...

We consider the problem of learning to act in partially observable, continuous-state-and-action worlds where we have abstract prior knowledge about the structure of the optimal policy in the form of a distribution over policies. Using ideas from planning-as-inference reductions and Bayesian unsupervised learning, we cast Markov Chain Monte Carlo as a stochastic, hill-climbing policy search algo...

We describe the use of a Genetic Algorithm (GA) for the Unit Selection problem, which is essentially a search/optimization problem. The various operators for the GA have been defined and comparison with optimization reached by hill climbing approaches is presented.

Truss optimization problem has been vastly studied during the past 30 years and many different methods have been proposed for this problem. Even though most of these methods assume that the design variables are continuously valued, in reality, the design variables of optimization problems such as cross-sectional areas are discretely valued. In this paper, an improved hill climbing and an improv...