113,065 research outputs found
The 1/5-th Rule with Rollbacks: On Self-Adjustment of the Population Size in the GA
Self-adjustment of parameters can significantly improve the performance of
evolutionary algorithms. A notable example is the
genetic algorithm, where the adaptation of the population size helps to achieve
the linear runtime on the OneMax problem. However, on problems which interfere
with the assumptions behind the self-adjustment procedure, its usage can lead
to performance degradation compared to static parameter choices. In particular,
the one fifth rule, which guides the adaptation in the example above, is able
to raise the population size too fast on problems which are too far away from
the perfect fitness-distance correlation.
We propose a modification of the one fifth rule in order to have less
negative impact on the performance in scenarios when the original rule reduces
the performance. Our modification, while still having a good performance on
OneMax, both theoretically and in practice, also shows better results on linear
functions with random weights and on random satisfiable MAX-SAT instances.Comment: 17 pages, 2 figures, 1 table. An extended two-page abstract of this
work will appear in proceedings of the Genetic and Evolutionary Computation
Conference, GECCO'1
A Study in function optimization with the breeder genetic algorithm
Optimization is concerned with the finding of global optima
(hence the name) of problems that can be cast in the form of a
function of several variables and constraints thereof. Among the
searching methods, {em Evolutionary Algorithms} have been shown to be
adaptable and general tools that have often outperformed traditional
{em ad hoc} methods. The {em Breeder Genetic Algorithm} (BGA)
combines a direct representation with a nice conceptual
simplicity. This work contains a general description of the algorithm
and a detailed study on a collection of function optimization
tasks. The results show that the BGA is a powerful and reliable
searching algorithm. The main discussion concerns the choice of
genetic operators and their parameters, among which the family of
Extended Intermediate Recombination (EIR) is shown to stand out. In
addition, a simple method to dynamically adjust the operator is
outlined and found to greatly improve on the already excellent overall
performance of the algorithm.Postprint (published version
A System for Accessible Artificial Intelligence
While artificial intelligence (AI) has become widespread, many commercial AI
systems are not yet accessible to individual researchers nor the general public
due to the deep knowledge of the systems required to use them. We believe that
AI has matured to the point where it should be an accessible technology for
everyone. We present an ongoing project whose ultimate goal is to deliver an
open source, user-friendly AI system that is specialized for machine learning
analysis of complex data in the biomedical and health care domains. We discuss
how genetic programming can aid in this endeavor, and highlight specific
examples where genetic programming has automated machine learning analyses in
previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and
Practice 2017 worksho
The detection of globular clusters in galaxies as a data mining problem
We present an application of self-adaptive supervised learning classifiers
derived from the Machine Learning paradigm, to the identification of candidate
Globular Clusters in deep, wide-field, single band HST images. Several methods
provided by the DAME (Data Mining & Exploration) web application, were tested
and compared on the NGC1399 HST data described in Paolillo 2011. The best
results were obtained using a Multi Layer Perceptron with Quasi Newton learning
rule which achieved a classification accuracy of 98.3%, with a completeness of
97.8% and 1.6% of contamination. An extensive set of experiments revealed that
the use of accurate structural parameters (effective radius, central surface
brightness) does improve the final result, but only by 5%. It is also shown
that the method is capable to retrieve also extreme sources (for instance, very
extended objects) which are missed by more traditional approaches.Comment: Accepted 2011 December 12; Received 2011 November 28; in original
form 2011 October 1
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