In most traditional machine learning algorithms, the training and testing datasets have
identical distributions and feature spaces. However, these assumptions have not held in …

Using the semi-tensor product of matrices, this paper investigates the maximum (weight)
stable set and vertex coloring problems of graphs with application to the group consensus of …

This paper proposes a multi-objective phasor measurement units (PMUs) placement method
in electric transmission grids. Further consideration is devoted to the early PMU placement …

Grouping problems aim to partition a set of items into multiple mutually disjoint subsets
according to some specific criterion and constraints. Grouping problems cover a large class …

The DNA microarrays are used to monitor the expression levels of significant genes. Most of
the microarray data are assumed to be high dimensional, redundant, and noisy. This paper …

In recent years, various heuristic optimization methods have been developed. Many of these
methods are inspired by swarm behaviors in nature, such as particle swarm optimization …

This book is written for computer engineers, scientists, and students studying/working in
reinforcement learning and artificial intelligence domains. The book collects recent …

An update scheme of the state probability vector of actions is critical for learning automata
(LA). The most popular is the pursuit scheme that pursues the estimated optimal action and …

This paper presents an adaptive bi-flight cuckoo search algorithm for continuous dynamic
optimization problems. Unlike the standard cuckoo search which relies on Levy flight, the …

Recently, several studies have been conducted to design mobility-based multicast routing
protocols for wireless mobile ad hoc networks (MANET). These protocols assume that the …