This chapter reviews applications of Memetic Algorithms in the areas of business analytics
and data science. This approach originates from the need to address optimization problems …

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

Community structure is an important feature in complex networks which has great significant
for organization of networks. The community detection is the process of partitioning the …

Learning automaton (LA) as one of artificial intelligence techniques is a stochastic model
operating in the framework of the reinforcement learning. LA has been found to be a useful …

One of the problems with traditional genetic algorithms (GAs) is premature convergence,
which makes them incapable of finding good solutions to the problem. The memetic …

Vertex coloring problem is a combinatorial optimization problem in graph theory in which a
color is assigned to each vertex of graph such that no two adjacent vertices have the same …

Genetic algorithms (GAs) can be divided into two broad approaches, Michigan and
Pittsburgh Approaches. In Pittsburg approach, one of the individuals (chromosomes) in the …

The wavefront cellular learning automata (WCLA), which is a generalization of
asynchronous cellular learning automata equipped with a diffusion capability, is a new …

Handoff and cabling costs management plays an important role in the design of cellular
mobile networks. Efficient assigning of cells to switches can have a significant impact on …

Learning automata (LA), which intellectually explores its optimal state by interacting with an
external environment continuously, is encountered widely in artificial intelligence. In the …