Showing papers by "Enrique Alba published in 2000"
TL;DR: This paper investigates the influence of migration frequency and migrant selection in a ring of islands running either steady-state, generational, or cellular GAs, and points out the considerable benefits that can accrue from asynchronous migration.
Abstract: Parallel genetic algorithms (PGAs) have been traditionally used to overcome the intense use of CPU and memory that serial GAs show in complex problems Non-parallel GAs can be classified into two classes: panmictic and structured-population algorithms The difference lies in whether any individual in the population can mate with any other one or not In this work, they are both considered as two reproductive loop types executed in the islands of a parallel distributed GA Our aim is to extend the existing studies from more conventional sequential islands to other kinds of evolution A key issue in such a coarse grain PGA is the migration policy, since it governs the exchange of individuals among the islands This paper investigates the influence of migration frequency and migrant selection in a ring of islands running either steady-state, generational, or cellular GAs A diversity analysis is also offered from an entropy point of view The study uses different problem types, namely easy, deceptive, multimodal, NP-Complete, and epistatic search landscapes in order to provide a wide spectrum of problem difficulties to support the results Large isolation values and random selection of the migrants are demonstrated as providing a larger probability of success and a smaller number of visited points Also, interesting observations on the relative performance of the different models are offered, as well as we point out the considerable benefits that can accrue from asynchronous migration
78 citations
18 Sep 2000
TL;DR: It is found that, with the same neighborhood, rectangular grids have some advantages in multimodal and epistatic problems, while square ones are more efficient for solving deceptive problems and for simple function optimization.
Abstract: Spatially structured evolutionary algorithms (EAs) have shown to be endowed with useful features for global optimization. Distributed EAs (dEA) and cellular EAs (cEA) are two of the most widely known types of structured algorithms. In this paper we deal with cellular EAs. Two important parameters guiding the search in a cEA are the population topology and the neighborhood defined on it. Here we first review some theoretical results which show that a cEA with a 2D grid can be easily tuned to shift from exploration to exploitation. We initially make a study on the relationship between the topology and the neighborhood by defining a ratio measure between they two. Then, we encompass a set of tests aimed at discovering the performance that different ratio values have on different classes of problems. We find out that, with the same neighborhood, rectangular grids have some advantages in multimodal and epistatic problems, while square ones are more efficient for solving deceptive problems and for simple function optimization. Finally, we propose and study a cEA in which the ratio is dynamically changed.
78 citations