Variation Rate to Maintain Diversity in Decision Space within Multi-Objective Evolutionary Algorithms

Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a du...

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...

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.