3 research outputs found
Using Dissortative Mating Genetic Algorithms to Track the Extrema of Dynamic Deceptive Functions
Traditional Genetic Algorithms (GAs) mating schemes select individuals for
crossover independently of their genotypic or phenotypic similarities. In
Nature, this behaviour is known as random mating. However, non-random schemes -
in which individuals mate according to their kinship or likeness - are more
common in natural systems. Previous studies indicate that, when applied to GAs,
negative assortative mating (a specific type of non-random mating, also known
as dissortative mating) may improve their performance (on both speed and
reliability) in a wide range of problems. Dissortative mating maintains the
genetic diversity at a higher level during the run, and that fact is frequently
observed as an explanation for dissortative GAs ability to escape local optima
traps. Dynamic problems, due to their specificities, demand special care when
tuning a GA, because diversity plays an even more crucial role than it does
when tackling static ones. This paper investigates the behaviour of
dissortative mating GAs, namely the recently proposed Adaptive Dissortative
Mating GA (ADMGA), on dynamic trap functions. ADMGA selects parents according
to their Hamming distance, via a self-adjustable threshold value. The method,
by keeping population diversity during the run, provides an effective means to
deal with dynamic problems. Tests conducted with deceptive and nearly deceptive
trap functions indicate that ADMGA is able to outperform other GAs, some
specifically designed for tracking moving extrema, on a wide range of tests,
being particularly effective when speed of change is not very fast. When
comparing the algorithm to a previously proposed dissortative GA, results show
that performance is equivalent on the majority of the experiments, but ADMGA
performs better when solving the hardest instances of the test set.Comment: Technical report complementing Carlos Fernandes' Ph
Modeling browser-based distributed evolutionary computation systems
From the era of big science we are back to the "do it yourself", where you do
not have any money to buy clusters or subscribe to grids but still have
algorithms that crave many computing nodes and need them to measure
scalability. Fortunately, this coincides with the era of big data, cloud
computing, and browsers that include JavaScript virtual machines. Those are the
reasons why this paper will focus on two different aspects of volunteer or
freeriding computing: first, the pragmatic: where to find those resources,
which ones can be used, what kind of support you have to give them; and then,
the theoretical: how evolutionary algorithms can be adapted to an environment
in which nodes come and go, have different computing capabilities and operate
in complete asynchrony of each other. We will examine the setup needed to
create a very simple distributed evolutionary algorithm using JavaScript and
then find a model of how users react to it by collecting data from several
experiments featuring different classical benchmark functions.Comment: Technical repor
Using Dissortative Mating Genetic Algorithms to Track the Extrema of Dynamic Deceptive Functions
Traditional Genetic Algorithms (GAs) mating schemes select individuals for crossover independently of their genotypic or phenotypic similarities. In Nature, this behaviour is known as random mating. However, non-random schemes − in which individuals mate according to their kinship or likeness − are more common in natural systems. Previous studies indicate that, when applied to GAs, negative assortative mating (a specific type of non-random mating, also known as dissortative mating) may improve their performance (on both speed and reliability) in a wide range of problems. Dissortative mating maintains the genetic diversity at a higher level during the run, and that fact is frequently observed as an explanation for dissortative GAs ability to escape local optima traps. Dynamic problems, due to their specificities, demand special care when tuning a GA, because diversity plays an even more crucial role than it does when tackling static ones. This paper investigates the behaviour of dissortative mating GAs, namely the recently proposed Adaptive Dissortative Mating GA (ADMGA), on dynamic trap functions. ADMGA selects parents according to their Hamming distance, via a self-adjustable threshold value. The method, by keeping population diversity during the run, provides an effective means to deal with dynamic problems. Tests conducted with deceptive and nearly deceptive trap functions indicate that ADMGA is able to outperform other GAs, some specifically designed for tracking moving extrema, on a wide range of tests, being particularly effective when speed of change is not very fast. When comparing the algorithm to a previously proposed dissortative GA, results show that performance is equivalent on the majority of the experiments, but ADMGA performs better when solving the hardest instances of the test set