546 research outputs found
On the Analysis of Simple Genetic Programming for Evolving Boolean Functions
This work presents a first step towards a systematic time and space complexity analysis of genetic programming (GP) for evolving functions with desired input/output behaviour. Two simple GP algorithms, called (1+1) GP and (1+1) GP*, equipped with minimal function (F) and terminal (L) sets are considered for evolving two standard classes of Boolean functions. It is rigorously proved that both algorithms are efficient for the easy problem of evolving conjunctions of Boolean variables with the minimal sets. However, if an extra function (i.e. NOT) is added to F, then the algorithms require at least exponential time to evolve the conjunction of n variables. On the other hand, it is proved that both algorithms fail at evolving the difficult parity function in polynomial time with probability at least exponentially close to 1. Concerning generalisation, it is shown how the quality of the evolved conjunctions depends on the size of the training set s while the evolved exclusive disjunctions generalize equally badly independent of s
Pattern Learning for Detecting Defect Reports and Improvement Requests in App Reviews
Online reviews are an important source of feedback for understanding
customers. In this study, we follow novel approaches that target this absence
of actionable insights by classifying reviews as defect reports and requests
for improvement. Unlike traditional classification methods based on expert
rules, we reduce the manual labour by employing a supervised system that is
capable of learning lexico-semantic patterns through genetic programming.
Additionally, we experiment with a distantly-supervised SVM that makes use of
noisy labels generated by patterns. Using a real-world dataset of app reviews,
we show that the automatically learned patterns outperform the manually created
ones, to be generated. Also the distantly-supervised SVM models are not far
behind the pattern-based solutions, showing the usefulness of this approach
when the amount of annotated data is limited.Comment: Accepted for publication in the 25th International Conference on
Natural Language & Information Systems (NLDB 2020), DFKI Saarbr\"ucken
Germany, June 24-26 202
Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling
Satellite imagery and remote sensing provide explanatory variables at
relatively high resolutions for modeling geospatial phenomena, yet regional
summaries are often desirable for analysis and actionable insight. In this
paper, we propose a novel method of inducing spatial aggregations as a
component of the machine learning process, yielding regional model features
whose construction is driven by model prediction performance rather than prior
assumptions. Our results demonstrate that Genetic Programming is particularly
well suited to this type of feature construction because it can automatically
synthesize appropriate aggregations, as well as better incorporate them into
predictive models compared to other regression methods we tested. In our
experiments we consider a specific problem instance and real-world dataset
relevant to predicting snow properties in high-mountain Asia
Open-ended evolution to discover analogue circuits for beyond conventional applications
This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s10710-012-9163-8. Copyright @ Springer 2012.Analogue circuits synthesised by means of open-ended evolutionary algorithms often have unconventional designs. However, these circuits are typically highly compact, and the general nature of the evolutionary search methodology allows such designs to be used in many applications. Previous work on the evolutionary design of analogue circuits has focused on circuits that lie well within analogue application domain. In contrast, our paper considers the evolution of analogue circuits that are usually synthesised in digital logic. We have developed four computational circuits, two voltage distributor circuits and a time interval metre circuit. The approach, despite its simplicity, succeeds over the design tasks owing to the employment of substructure reuse and incremental evolution. Our findings expand the range of applications that are considered suitable for evolutionary electronics
Closed-loop separation control over a sharp edge ramp using Genetic Programming
We experimentally perform open and closed-loop control of a separating
turbulent boundary layer downstream from a sharp edge ramp. The turbulent
boundary layer just above the separation point has a Reynolds number
based on momentum thickness. The goal of the
control is to mitigate separation and early re-attachment. The forcing employs
a spanwise array of active vortex generators. The flow state is monitored with
skin-friction sensors downstream of the actuators. The feedback control law is
obtained using model-free genetic programming control (GPC) (Gautier et al.
2015). The resulting flow is assessed using the momentum coefficient, pressure
distribution and skin friction over the ramp and stereo PIV. The PIV yields
vector field statistics, e.g. shear layer growth, the backflow area and vortex
region. GPC is benchmarked against the best periodic forcing. While open-loop
control achieves separation reduction by locking-on the shedding mode, GPC
gives rise to similar benefits by accelerating the shear layer growth.
Moreover, GPC uses less actuation energy.Comment: 24 pages, 24 figures, submitted to Experiments in Fluid
Metamodel-assisted design optimization of piezoelectric flex transducer for maximal bio-kinetic energy conversion
Energy Harvesting Devices (EHD) have been widely used to generate electrical power from the bio-kinetic energy of human body movement. A novel Piezoelectric Flex Transducer (PFT) based on the Cymbal device has been proposed by Daniels et al. (2013) for the purpose of energy harvesting. To further improve the efficiency of the device, optimal design of the PFT for maximum output power subject to stress and displacement constraints is carried out in this paper. Sequential Quadratic Programming (SQP) on metamodels generated with Genetic Programming from a 140-point optimal Latin hypercube design of experiments is used in the optimization. Finally, the optimal design is validated by finite element simulations. The simulations show that the magnitude of the electrical power generated from this optimal PFT harvesting device can be up to 6.5 mw when a safety design factor of 2.0 is applied
Optimization of supply diversity for the self-assembly of simple objects in two and three dimensions
The field of algorithmic self-assembly is concerned with the design and
analysis of self-assembly systems from a computational perspective, that is,
from the perspective of mathematical problems whose study may give insight into
the natural processes through which elementary objects self-assemble into more
complex ones. One of the main problems of algorithmic self-assembly is the
minimum tile set problem (MTSP), which asks for a collection of types of
elementary objects (called tiles) to be found for the self-assembly of an
object having a pre-established shape. Such a collection is to be as concise as
possible, thus minimizing supply diversity, while satisfying a set of stringent
constraints having to do with the termination and other properties of the
self-assembly process from its tile types. We present a study of what we think
is the first practical approach to MTSP. Our study starts with the introduction
of an evolutionary heuristic to tackle MTSP and includes results from extensive
experimentation with the heuristic on the self-assembly of simple objects in
two and three dimensions. The heuristic we introduce combines classic elements
from the field of evolutionary computation with a problem-specific variant of
Pareto dominance into a multi-objective approach to MTSP.Comment: Minor typos correcte
Learning to trade in an unbalanced market
We study the evolution of trading strategies in double auctions as the size of the market gets larger. When the number of buyers and sellers is balanced, Fano et al.~(2011) show that the choice of the order-clearing rule (simultaneous or asynchronous) steers the emergence of fundamentally different strategic behavior. We extend their work to unbalanced markets, confirming their main result as well as that allocative inefficiency tends to zero. On the other hand, we discover that convergence to the competitive outcome takes place only when the market is large and that the long side of the market is more effective at improving its disadvantaged terms of trade under asynchronous order-clearing
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