Maintaining population diversity is critical to the performance of a Genetic Algorithm (GA).
Applying appropriate strategies for measuring population diversity is important in order to …

A genetic algorithm (GA) experiences premature convergence when the diversity is lost in
the population. Adaptive GAs aim to maintain diversity in the population by trading off a …

The island model allows genetic algorithms to effectively maintain diversity through
migration between multiple independent populations. Due to its flexibility and modularity, it …

The genetic algorithm plays a very important role in many areas of applications. In this
research, the authors propose to accelerate the evolution speed of the genetic algorithm by …

As a popular type of parallel evolutionary algorithms, distributed evolutionary algorithms
(DEAs) are widely used in a variety of fields. To get better solutions of concrete problems, a …

We compare the two parent selection methods “linear rank” and “tournament” in a Genetic
Algorithm applied to a dynamic Travelling Salesman Problem (TSP). The inherent dynamics …

Genetic Algorithms (GAs) are being used to solve a wide range of problems in real world
problems, and it is important to study their implementations to improve the solution quality …

While the main motivation for Parallel Genetic Algorithms (PGAs) has been to improve the
scalability of Genetic Algorithms (GAs), techniques and strategies for maintaining population …

Despite the intuition that the same population size is not needed throughout the run of an
Evolutionary Algorithm (EA), most EAs use a fixed population size. This paper presents an …

Genetic Algorithm (GA) has been proved to be efficient at searching optimal solution among
a large and complex search space in an adaptable way. The traditional GA doesn't provide …