This paper presents two variants of Genetic Algorithms (GAs) for solving the Multiple Container Packing Problem (MCPP), which is a combinatorial optimization problem comprising similarities to the Knapsack Problem and the Bin Packing Problem. Two different representation schemes are suggested, namely direct encoding and order based encoding. While order based encoded solutions are always feasib...

This paper presents a sampling-based method for path planning in robotic systems without known cost-to-go information. It uses trajectories generated from random search to heuristically learn the cost-to-go of regions within the configuration space. Gradually, the search is increasingly directed towards lower cost regions of the configuration space, thereby producing paths that converge towards...

A New features are extracted and compared to improve the prediction of protein-protein interactions. The basic idea is to select and use the best set of features from the Tensor matrices that are produced by the frequency vectors of the protein sequences. Three set of features are compared, the first set is based on the indices that are the most common in the interacting proteins, the second se...

This paper presents a genetic algorithm based on a strategy to find the shortest path in a network with variable length chromosomes (strings) and their genes have been used for encoding the problem. Crossover and mutation together provide improved quality of solution. Even though shortest path algorithms are already well established, this paper proposing a simple method of finding shortest path...

We show that the recently introduced logarithmic metrics used to predict disease arrival times on complex networks are approximations of more general network-based measures derived from random walks theory. Using the daily air-traffic transportation data we perform numerical experiments to compare the infection arrival time with this alternative metric that is obtained by accounting for multipl...