4 research outputs found
Control of Bloat in Genetic Programming by Means of the Island Model
This paper presents a new proposal for reducing bloat in Genetic
Programming. This proposal is based in a well-known parallel evolutionary
model: the island model. We firstly describe the theoretical motivation for this
new approach to the bloat problem, and then we present a set of experiments
that gives us evidence of the findings extracted from the theory. The experiments
have been performed on a representative problem extracted from the GP
field: the even parity 5 problem. We analyse the evolution of bloat employing
different settings for the parameters employed. The conclusion is that the Island
Model helps to prevent the bloat phenomenon
It is Time for New Perspectives on How to Fight Bloat in GP
The present and future of evolutionary algorithms depends on the proper use
of modern parallel and distributed computing infrastructures. Although still
sequential approaches dominate the landscape, available multi-core, many-core
and distributed systems will make users and researchers to more frequently
deploy parallel version of the algorithms. In such a scenario, new
possibilities arise regarding the time saved when parallel evaluation of
individuals are performed. And this time saving is particularly relevant in
Genetic Programming. This paper studies how evaluation time influences not only
time to solution in parallel/distributed systems, but may also affect size
evolution of individuals in the population, and eventually will reduce the
bloat phenomenon GP features. This paper considers time and space as two sides
of a single coin when devising a more natural method for fighting bloat. This
new perspective allows us to understand that new methods for bloat control can
be derived, and the first of such a method is described and tested.
Experimental data confirms the strength of the approach: using computing time
as a measure of individuals' complexity allows to control the growth in size of
genetic programming individuals
Recognition and assessment of seafloor vegetation using a single beam echosounder
This study focuses on the potential of using a single beam echosounder as a tool for recognition and assessment of seafloor vegetation. Seafloor vegetation is plant benthos and occupies a large portion of the shallow coastal bottoms. It plays a key role in maintaining the ecological balance by influencing the marine and terrestrial worlds through interactions with its surrounding environment. Understanding of its existence on the seafloor is essential for environmental managers.Due to the important role of seafloor vegetation to the environment, a detailed investigation of acoustic methods that can provide effective recognition and assessment of the seafloor vegetation by using available sonar systems is necessary. One of the frequently adopted approaches to the understanding of ocean environment is through the mapping of the seafloor. Available acoustic techniques vary in kinds and are used for different purposes. Because of the wide scope of available techniques and methods which can be employed in the field, this study has limited itself to sonar techniques of normal incidence configuration relative to seafloors in selected regions and for particular marine habitats. For this study, a single beam echosounder operating at two frequencies was employed. Integrated with the echosounder was a synchronized optical system. The synchronization mechanism between the acoustic and optical systems provided capabilities to have very accurate groundtruth recordings for the acoustic data, which were then utilized as a supervised training data set for the recognition of seafloor vegetation.In this study, results acquired and conclusions made were all based on the comparison against the photographic recordings. The conclusion drawn from this investigation is only as accurate as within the selected habitat types and within very shallow water regions.In order to complete this study, detailed studies of literature and deliberately designed field experiments were carried out. Acoustic data classified with the help of the synchronized optical system were investigated by several methods. Conventional methods such as statistics and multivariate analyses were examined. Conventional methods for the recognition of the collected data gave some useful results but were found to have limited capabilities. When seeking for more robust methods, an alternative approach, Genetic Programming (GP), was tested on the same data set for comparison. Ultimately, the investigation aims to understand potential methods which can be effective in differentiating the acoustic backscatter signals of the habitats observed and subsequently distinguishing between the habitats involved in this study
A Study of Ordered Gene Problems Featuring DNA Error Correction and DNA Fragment Assembly with a Variety of Heuristics, Genetic Algorithm Variations, and Dynamic Representations
Ordered gene problems are a very common classification of optimization problems.
Because of their popularity countless algorithms have been developed in an attempt
to find high quality solutions to the problems. It is also common to see many different
types of problems reduced to ordered gene style problems as there are many popular
heuristics and metaheuristics for them due to their popularity.
Multiple ordered gene problems are studied, namely, the travelling salesman problem,
bin packing problem, and graph colouring problem. In addition, two bioinformatics
problems not traditionally seen as ordered gene problems are studied: DNA error
correction and DNA fragment assembly. These problems are studied with multiple
variations and combinations of heuristics and metaheuristics with two distinct types
or representations. The majority of the algorithms are built around the Recentering-
Restarting Genetic Algorithm.
The algorithm variations were successful on all problems studied, and particularly
for the two bioinformatics problems. For DNA Error Correction multiple cases were
found with 100% of the codes being corrected. The algorithm variations were also
able to beat all other state-of-the-art DNA Fragment Assemblers on 13 out of 16
benchmark problem instances