The rapid increase of dimensions and complexity of real life problems makes it more difficult to find optimal solutions by traditional optimization methods. This challenge requires intelligent and sophisticated algorithms to make the right decisions given a set of inputs and a variety of possible actions. In the problem solving arena, this definition is transformed into the term of artificial i...

Published as a chapter in Approximation Algorithms and Metaheuristics, a book edited by T. F. Gonzalez.

Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order to record experiences and allow project to be replicated, a standard process as a methodology for designing and implementing metaheuristic algorithms is necessary. To the best of the authors’ knowledge, no methodology has been proposed in literature ...

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithm...

The ant colony optimisation metaheuristic has shown promise on simplified artificial instances of university course timetabling problems. However, limited work has been done applying it to practical timetabling problems. In this paper, we describe the application of the ant colony optimisation to a highly constrained real–world instance of the university course timetabling problem. We present t...

Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a fitness function and a set of solutions in the search space are evaluated with respect to the fitness function using a metaheuristic search technique. The application of metaheuristic search techniques for testing is promising due to f...

Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a fitness function and a set of solutions in the search space are evaluated with respect to the fitness function using a metaheuristic search technique. The application of metaheuristic search techniques for testing is promising due to t...

Two metaheuristic algorithms namely Artificial Immune Systems (AIS) and Genetic Algorithms are classified as computational systems inspired by theoretical immunology and genetics mechanisms. In this work we examine the comparative performances of two algorithms. A special selection algorithm, Clonal Selection Algorithm (CLONALG), which is a subset of Artificial Immune Systems, and Genetic Algor...

The problem of detecting suitable parameters for metaheuristic optimization algorithms is well known long since. As these nondeterministic methods, e.g. evolution strategies (ES) [1], are highly adaptible to a specific application, detecting good parameter settings is vital for their success. Performance differences of orders of magnitude (in time and/or quality) are often achieved by means of ...

This paper describes a new approach to the generation of referring expressions. We propose to formalize a scene as a labeled directed graph and describe content selection as a subgraph construction problem. Cost functions are used to guide the search process and to give preference to some solutions over others. The resulting graph algorithm can be seen as a meta-algorithm in the sense that defi...