Deceptive problems have always been considered difficult for Genetic Algorithms. To cope with this characteristic, the literature has proposed the use of Parallel Genetic Algorith...
Within the parallel genetic algorithm framework, there currently exists a growing dichotomy between coarse-pain and fine-grain parallel architectures. This paper attempts to chara...
Parallel genetic algorithms (PGAs) have been developed to reduce the large execution times that are associated with serial genetic algorithms (SGAs). They have also been used to s...
Lee Wang, Anthony A. Maciejewski, Howard Jay Siege...
—Genetic Algorithms (GAs) are powerful search techniques that are used to solve difficult problems in many disciplines. Unfortunately, they can be very demanding in terms of com...
This paper presents a study of parallel genetic algorithms (GAs) with multiple populations (also called demes or islands). The study makes explicit the relation between the probab...
Abstract. Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. This paper describes how finegrained parallel genetic algorithms can be ...
Genetic algorithms (GAs) are stochastic search methods that have been successfully applied in many search, optimization, and machine learning problems. Their parallel counterpart (...