Automatic Inference of Fault Tree Models Via Multi-Objective Evolutionary Algorithms

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...

Automatic Design of Evolutionary Algorithms for Multi-Objective Combinatorial Optimization

Multi-objective evolutionary algorithms (MOEAs) have been the subject of a large research effort over the past two decades. Traditionally, these MOEAs have been seen as monolithic units, and their study was focused on comparing them as blackboxes. More recently, a component-wise view of MOEAs has emerged, with flexible frameworks combining algorithmic components from different MOEAs. The number...

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.