Gene selection aims at identifying a (small) subset of informative genes from the initial data in order to obtain high predictive accuracy for classification. Gene selection can be considered as a combinatorial search problem and thus be conveniently handled with optimization methods. In this article, we summarize some recent developments of using metaheuristic-based methods within an embedded ...

Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we d...

Existing automated test data generation techniques tend to start from scratch, implicitly assuming no pre-existing test data are available. However, this assumption may not always hold, and where it does not, there may be a missed opportunity; perhaps the pre-existing test cases could be used to assist the automated generation of additional test cases. This paper introduces search-based test da...

Model-Driven Engineering (MDE) is a software engineering approach that treats models as the primary development artefacts. Models are manipulated, using model management operations (MMO), in order to perform engineering tasks such as comparison, validation, and code generation. We propose that many of the challenges faced in MDE could be addressed using Search-Based Software Engineering (SBSE) ...

In this paper, we describe a new incomplete approach for solving constraint satisfaction problems (CSPs) based on the ant colony optimization (ACO) metaheuristic. The idea is to use artificial ants to keep track of promising areas of the search space by laying trails of pheromone. This pheromone information is used to guide the search, as a heuristic for choosing values to be assigned to variab...

This paper presents a tabu search metaheuristic algorithm for the automatic generation of structural software tests. It is a novel work since tabu search is applied to the automation of the test generation task, whereas previous works have used other techniques such as genetic algorithms. The developed test generator has a cost function for intensifying the search and another for diversifying t...

This paper is concerned with an aspect of the design of metaheuristic algorithms, such as evolutionary algorithms, tabu search and ant colony optimization. The topic that is considered is how problems can be represented when they are given to a metaheuristic algorithm. A particular difficulty is presented, viz. the “bottleneck”, where the problem is artificially converted into a new representat...

Multiple unique input/output sequences (UIOs) are often used to generate robust and compact test sequences in finite state machine (FSM) based testing. However, computing UIOs is NP-hard. Metaheuristic optimisation techniques (MOTs) such as genetic algorithms (GAs) and simulated annealing (SA) are effective in providing good solutions for some NP-hard problems. In the paper, the authors investi...

In metaheuristic optimization, understanding the relationship between problems and algorithms is important but non-trivial. There has been a growing interest in the literature on techniques for analysing problems, however previous work has mainly been developed for discrete problems. In this paper, we develop a novel framework for characterising continuous optimization problems based on the con...

Currently, there exist several offline calibration techniques that can be used to fine-tune the parameters of a metaheuristic. Such techniques require, however, to perform a considerable number of independent runs of the metaheuristic in order to obtain meaningful information. Here, we are interested on the use of this information for assisting the algorithm designer to discard components of a ...