We give a detailed analysis of the optimization time of the (1 + 1)-Evolutionary Algorithm under two simple fitness functions (OneMax and LeadingOnes). The problem has been approached in the evolutionary algorithm literature in various ways and with different degrees of rigor. Our asymptotic approximations for the mean and the variance represent the strongest of their kind. The approach we deve...

In this paper, we explore the automatic explanation of Multivariate Time Series (MTS) through learning Dynamic Bayesian Networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of process MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for s...

A key contributing factor to incredible success of deep neural networks has been the significant rise on massively parallel computing devices allowing researchers to greatly increase the size and depth of deep neural networks, leading to significant improvements in modeling accuracy. Although deeper, larger, or complex deep neural networks have shown considerable promise, the computational comp...

Modern CAD tools must represent large Boolean functions compactly in order to obtain reasonable runtimes for synthesis and veriication. The Shared Binary Decision Diagram (SBDD) with negative edge attributes can represent many functions in a compact form if a proper variable ordering is used. In this work we describe a technique for reordering the variables in an SBDD to reduce the size of the ...

Bayesian evolutionary analysis provide a statistically sound and flexible framework for estimation of evolutionary parameters. In this method, posterior estimates of evolutionary rate (μ) are derived by combining evolutionary information in the data with researcher’s prior knowledge about the true value of μ. Nucleotide sequence samples of fast evolving pathogens that are taken at d...

the current study investigated and compared data generation methods for groundwater modeling. thesemethods can be divided into two classes; geostatistic and probabilistic. by comparing geostatistic methods,the best method was chosen and the hydraulic conductivity of the aquifer was generated for a study area ina grid (9-cell) format. after observing weak spatial correlation between the data, th...

On our crowded roads, drivers must compete for space but cooperate to avoid occupying the same space at the same time. Decision-making is strategic and requires mutual understanding of other’s choices. Fully autonomous vehicles (AVs) will need risk management software to make these types strategic decisions without human arbitration. Accidents will occur, and what constitutes rational and ‘safe...

Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning tasks, TD methods require a function approximator to represent the value function. However, using function approximators requires manually making crucial representational decisions. This paper investigates evolutionary...

MOTIVATION Correlated events of gains and losses enable inference of co-evolution relations. The reconstruction of the co-evolutionary interactions network in prokaryotic species may elucidate functional associations among genes. RESULTS We developed a novel probabilistic methodology for the detection of co-evolutionary interactions between pairs of genes. Using this method we inferred the co...