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Optimization problems with continuous variables are very often in real-life scenarios. This is why several studies have focused on the use of metaheuristics to solve these problems, known as continuous optimization problems. This chapter focuses on the robust population-based metaheuristic Variable Mesh Optimization (VMO). We review the fundamental aspects of VMO for continuous optimization. Moreover, we analyze the variants of VMO that have been proposed to solve multimodal problems, i.e. those with multiple optima at several search subspaces. The chapter uses a homogeneous algorithmic framework to describe VMO and its variants with niching strategies for multimodal problems.

This paper extends the Gamma associative classifier, making it able to deal with hybrid and incomplete data. In addition, it also encompasses the gamma rough sets model for dealing with such data, introducing the extended gamma rough sets. Some properties of such sets are demonstrated in this paper. In turn, the novel extended gamma rough sets are used to improve the extended gamma associative classifier by selecting the instances. The results indicate that the selection of instances significantly improves the accuracy of the extended gamma associative classifier while reducing its computational cost.

"Pre-processing the dataset is an important stage in the Knowledge Discovery in Datasets (KDD) process. Filtering noise through instance selection is a necessary task. With this, the risk to use misclassified and non-representative instances to train supervised classifiers is reduced. This study aims at improving the performance of the Gamma associative classifier, by introducing a novel similarity function to guide instance selection. The experimental results, over 15 datasets, include several instance selection methods, and their influence in the performance of Gamma classifier is analyzed. The effectiveness of the proposed similarity function is tested, obtaining good results according to classifier accuracy and instance retention ratio."

This paper introduces the Gamma Rough Sets for management information systems where the universe objects are represented by continuous attributes and are connected by similarity relations. Some properties of such sets are demonstrated in this paper. In addition, Gamma Rough Sets are used to improve the Gamma associative classifier, by selecting instances. The results indicate that the selection of instances significantly reduces the computational cost of the Gamma classifier without affecting its effectiveness. The results also suggest that the selection of instances using Gamma Rough Sets favors other lazy learners, such as Nearest Neighbor and ALVOT.