ABSTRACT. Graph colorings are a major area of study in graph theory involving the constrained assignment of labels (colors) to vertices or edges. There are many types of colorings defined in the literature. The most common type of coloring is the proper vertex $k$-coloring which is defined as a vertex coloring from a set of $k$ colors such that no two adjacent vertices share a common color.

Our central focus in this paper is a variant of the proper vertex $k$-coloring problem, termed \emph{graceful coloring} introduced by Gary Chartrand in 2015. A \textsl{graceful $k$-coloring} of an undirected connected graph G is a proper vertex coloring using $k$ colors that induces a proper edge coloring, where the color for an edge $(u,v)$ is the absolute value of the difference between the colors assigned to vertices $u$ and $v$.

In this work we find the \emph{graceful chromatic number}, the minimum $k$ for which a graph has a graceful $k$-coloring, for some well-known graphs and classes of graphs, such as diamond graph, Petersen graph, Moser spindle graph, Goldner-Harary graph, friendship graphs, fan graphs and others.

ABSTRACT. Cellular Automata (CAs) represent important tools in the study of complex systems and interactions. CAs are decentralized structures of simple and locally interacting cells that evolve following a set of rules. The problem of finding CA rules able to generate a desired global behaviour is highly challenging because only local information influences the cell evolution. Having this goal in mind, this talk presents both abstract problems (such as Density Classification) and more practical ones (such as Image Segmentation), that have been approached in the context of Cellular Automata by using biologically inspired techniques and investigating new CA topologies.

ABSTRACT. The problem of Network Influence Maximization is approached by an Extremal Optimization algorithm called Shapley value Extremal Optimization (SvEO). The influence maximization problem for the independent cascade model is considered as a cooperative game in which players seek to choose seeder nodes to maximize the value of the game computed as the size of the influence set of their cascade model by maximizing their average marginal contribution to all possible player coalitions, i.e. subsets of the seeder set. SvEO is compared with other influence maximization algorithms by means of numerical experiments, with promising results. Possible implications of the use of the Shapley value are discussed using a network constructed from highly cited publication data in the field of computer science.

ABSTRACT. The Whale Optimization Algorithm (WOA) is a novel nature-inspired algorithm, being originally dedicated to continuous function optimization. This paper modifies it to address combinatorial, i.e. discrete function optimization; the new algorithm is called Combinatorial Whale Optimization Algorithm (cWOA). cWOA was applied to protein folding problem on the 2D HP Model, which is a well known combinatorial optimization problem. The results are encouraging for further development and expansion of experiments to 3D HP or other models.

ABSTRACT. This paper contains an investigation into the GA population dynamics induced by very high mutation operator probabilities (≈ 0.95). Drawing inspiration from Consensus Sequence Plots ([G. Ochoa, 2001]) and Estimation of Distribution Algorithms ([P. Larrañaga, 2001]), population distribution naı̈ve changes are computed between successive generations. This metric is used to characterise multiple parameter variants for a Simple Genetic Algorithm, contrasting low- and high- probability mutation, and low- and high- entropy mutation.

ABSTRACT. The current work in progress paper describes a proof of concept for an automatic fitness evaluator in an evolutionary music composition setting. The associated research project proposes a novel algorithmic music creation mechanism. It uses linear genetic programming to create short musical pieces statistically similar to real-world pieces from a corpus.

We present two fully automatic quality assessment methods for music, both used as fitness functions in the genetic algorithm: one proposed in a previous research stage, as well as a novel one involving n-grams. Experiments are proposed and described for comparing these measurement mechanisms to each other as well as to other automated methods present in the literature.

ABSTRACT. In this work, we are proposing a methodology for data-driven decision making using sentiment analysis. The analysis of sentiment is done by text mining the activity notes recorded in Customer Relationship Management Systems used to manage complex sales in business to business environments. We built the sentiment enhanced sales prediction models using Artificial Neural Networks, Support Vector Machines and Random Forests and involving different sentiment features. The approach produced meaningful results with Random Forest obtaining the best improvement compared to a baseline model without sentiment features. The best model showed that new attributes incorporating sentiment information improved the accuracy from a baseline of 85.15% to 89.11%. This model was used to conduct an analysis and an evaluation of the steps needed to be taken to win a possible losing deal in a real-world business to business customer relationship management system.