This paper presents a genetic algorithm and a scatter search procedure to solve the well-known job shop scheduling problem. In contrast to the single population search performed by the genetic algorithm, the scatter search algorithm splits the population of solutions in a diverse and high-quality set to exchange information between individuals in a controlled way. Extensions from a single to a ...

We present a new hybrid algorithm for data clustering. This new proposal uses one of the well known evolutionary algorithms called Scatter Search. Scatter Search operates on a small set of solutions and makes only a limited use of randomization for diversification when searching for globally optimal solutions. The proposed method discovers automatically cluster number and cluster centres withou...

This paper proposes the usage of visualisation widgets for exploratory search with sentiment as a facet. Starting from specific design goals for depiction of ambivalence in sentiment, two visualization widgets were implemented: scatter plot and parallel coordinates. Those widgets were evaluated against a text baseline in a small-scale usability study with exploratory tasks using Wikipedia as da...

Recently, there has been an interest in methods for filtering large scatter plot spaces for interesting patterns. However, user interaction remains crucial in starting an explorative analysis in a large scatter plot space. We introduce an approach for explorative search and navigation in large sets of scatter plot diagrams. By means of a sketch-based query interface, users can start the explora...

In the last decade job shop scheduling problems have been subject to intensive research due to their multiple applications. Job shop scheduling is known as a strongly NPcomplete problem. In the job shop scheduling problems, processing times and due dates are very dynamically due to both human and machine resource factors. Fuzzy sets are used to model the uncertain processing times and due dates...

This paper presents the scatter search particle filter (SSPF) algorithm and its application to real-time hands and face tracking. SSPF combines sequential Monte Carlo (particle filter) and combinatorial optimization (scatter search) methods. Hands and face are characterized using a skin-color model based on explicit RGB region definition. The hybrid SSPF approach enhances the performance of cla...

Scatter search is a population-based method that has been shown to yield high-quality outcomes for combinatorial optimization problems. It uses strategies for combining solution vectors that have proved effective in a variety of problem settings. In this paper, we present a scatter search implementation for an NP-hard variant of the classic p-hub median problem. Specifically, we tackle the unca...

We propose the use of scatter search with path relinking for the inference of phylogenetic trees. Solutions are here represented as trees whose leaves span the set of species under study. These trees are evaluated using a minimum weight criterion under the ultrametric model. The main features of this approach are the utilization of a crossover-based schema for diversification generation, the us...

Scatter Search and its generalized form Path Relinking, are evolutionary methods that have been successfully applied to hard optimization problems. Unlike genetic algorithms, they operate on a small set of solutions and employ diversification strategies of the form proposed in Tabu Search, which give precedence to strategic learning based on adaptive memory, with limited recourse to randomizati...

In this paper, a multiobjective scatter search procedure for a bi-objective territory design problem is proposed. A territory design problem consist of partitioning a set of basic units into larger groups that are suitable with respect to some specific planning criteria. These groups must be compact, connected, and balanced with respect to the number of customers and sales volume. The bi-object...