Intelligent vehicles are an active area of research, with significant contributions being made in sensing, control, and information processing. Advances in the field have the potential to improve safety, reduce risk of injury, and save lives. The focus of this research deals with the challenges of utilizing data from cameras looking out of the vehicle, monitoring the driving environment. We pro...

Genetic algorithms have proven to be a wellsuited technique for solving selected combinatorial optimization problems. When solving real-world problems, often the main task is to find a proper representation for the candidate solutions. Strings of elementary data types with standard genetic operators may tend to create infeasible individuals during the search because of the discrete and often co...

A multi-condition multi-objective optimization method that can find Pareto front over a defined condition space is developed for the first time using deep reinforcement learning. Unlike conventional methods which perform at single condition, present learns correlations between conditions and optimal solutions. The exclusive capability of examined in solutions novel modified Kursawe benchmark pr...

The Large Hadron Collider (LHC) experiments will collect unprecedented data volumes in the next Physics run, with high pile-up collisions resulting in events that require a complex processing. Hence, the collaborations have been required to update their Computing Models to optimize the use of the available resources and control the growth of resources, in the midst of widespread funding restric...

In this paper we, present a new heuristic called PowerFM which is a modification of the well-known Fidducia Mattheyeses algorithm for VLSI netlist partitioning. PowerFM considers the minimization of power consumption due to the nets cut. The advantages of using PowerFM as an initial solution generator for other iterative algorithms, in particular Genetic Algorithm (GA) and Tabu Search (TS), for...

Real world problems often present multiple, frequently conflicting, objectives. The research for optimal solutions of multi-objective problems can be achieved through means of genetic algorithms, which are inspired by the natural process of evolution: an initial population of solutions is randomly generated, then pairs of solutions are selected and combined in order to create new solutions slig...

Topic Modeling (TM) is a rapidly-growing area at the interfaces of text mining, artificial intelligence and statistical modeling, that is being increasingly deployed to address the ’information overload’ associated with extensive text repositories. The goal in TM is typically to infer a rich yet intuitive summary model of a large document collection, indicating a specific collection of topics t...

In this paper, we propose variational optimistic linear support (VOLS), a novel algorithm that finds bounded approximate solutions for multi-objective coordination graphs (MO-CoGs). VOLS builds and improves upon an existing exact algorithm called variable elimination linear support (VELS). Like VELS, VOLS solves a MO-CoG as a series of scalarized single-objective coordination graphs. We improve...