Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks

Multi-Objective Evolutionary Algorithms

Evolutionary Multi-Objective Algorithms

The versatility that genetic algorithm (GA) has proved to have for solving different problems, has make it the first choice of researchers to deal with new challenges. Currently, GAs are the most well known evolutionary algorithms, because their intuitive principle of operation and their relatively simple implementation; besides they have the ability to reflect the philosophy of evolutionary co...

Multi-Objective Evolutionary Algorithms

Real world optimization problems are often too complex to be solved through analytical means. Evolutionary algorithms, a class of algorithms that borrow paradigms from nature, are particularly well suited to address such problems. These algorithms are stochastic methods of optimization that have become immensely popular recently, because they are derivative-free methods, are not as prone to get...

Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks

As the pervasiveness of social networks increases, new NPhard related problems become interesting for the optimization community. The objective of influence maximization is to contact the largest possible number of nodes in a network, starting from a small set of seed nodes, and assuming a model for information propagation. This problem is of utmost practical importance for applications ranging...

Parallel Multi-objective Evolutionary Algorithms

This paper describes a general overview of parallel multi-objective evolutionary algorithms (MOEA) from the design and the implementation point of views. A unified taxonomy using three hierarchical parallel models is proposed. Different parallel architectures are considered. The performance evaluation issue of parallel MOEA is also discussed.