In almost no other field of computer science, the idea of using bio-inspired search paradigms
has been so useful as in solving multiobjective optimization problems. The idea of using a …

The demands of image processing related systems are robustness, high recognition rates,
capability to handle incomplete digital information, and magnanimous flexibility in capturing …

Imbalanced data with a skewed class distribution are common in many real-world
applications. Deep Belief Network (DBN) is a machine learning technique that is effective in …

The induction of decision trees is a widely-used approach to build classification models that
guarantee high performance and expressiveness. Since a recursive-partitioning strategy …

Classification on high-dimensional data with thousands to tens of thousands of dimensions
is a challenging task due to the high dimensionality and the quality of the feature set. The …

Domination-based sorting and decomposition are two basic strategies used in multiobjective
evolutionary optimization. This paper proposes a hybrid multiobjective evolutionary …

Class imbalance problems, where the number of samples in each class is unequal, is
prevalent in numerous real world machine learning applications. Traditional methods which …

We present an integrated algorithm for simultaneous feature selection (FS) and designing of
diverse classifiers using a steady state multiobjective genetic programming (GP), which …

Multi-objective optimization (MOO) is frequently used to solve many practical problems of
chemical processes but process designers only need a limited number of valuable solutions …

Smart Grid (SG) systems are critical, intelligent infrastructure utility services connected
through open networks that are potentially susceptible to cyber-attacks with very acute …