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 …

The classic combinatorial optimization problem of graph coloring is one of the most famous
NP-complete problems. One example of the graph coloring problem is the four-color map …

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 …

Graph coloring is a challenging combinatorial optimization problem with a wide range of
applications. In this paper, a distribution evolutionary algorithm based on a population of …

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 automaton (LA) as a promising technique of artificial intelligence is a self-adaptive
decision-making device that interacts with an unknown stochastic environment and is …

Learning automaton (LA) is one of the reinforcement learning techniques in artificial
intelligence. Learning automata's learning ability in unknown environments is a useful …

Genetic algorithms are well-known numerical optimizers used for a wide array of
applications. However, their performance when applied to combinatorial optimization …

Since many real problems have several limitations and constraints for different
environments, no standard optimization algorithms could work successfully for all kinds of …

In order to solve the problem of identification collision in multi-reader Radio Frequency
Identification (RFID) systems, this paper proposes a multi-channel anti-collision algorithm …