655 research outputs found
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks
The increasing inclusion of Deep Learning (DL) models in safety-critical
systems such as autonomous vehicles have led to the development of multiple
model-based DL testing techniques. One common denominator of these testing
techniques is the automated generation of test cases, e.g., new inputs
transformed from the original training data with the aim to optimize some test
adequacy criteria. So far, the effectiveness of these approaches has been
hindered by their reliance on random fuzzing or transformations that do not
always produce test cases with a good diversity. To overcome these limitations,
we propose, DeepEvolution, a novel search-based approach for testing DL models
that relies on metaheuristics to ensure a maximum diversity in generated test
cases. We assess the effectiveness of DeepEvolution in testing computer-vision
DL models and found that it significantly increases the neuronal coverage of
generated test cases. Moreover, using DeepEvolution, we could successfully find
several corner-case behaviors. Finally, DeepEvolution outperformed Tensorfuzz
(a coverage-guided fuzzing tool developed at Google Brain) in detecting latent
defects introduced during the quantization of the models. These results suggest
that search-based approaches can help build effective testing tools for DL
systems
Novel metaheuristic hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation
© 2014 Elsevier B.V. All rights reserved. This paper presents hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation and their application to control of a flexible manipulator system. Spiral dynamic algorithm (SDA) has faster convergence speed and good exploitation strategy. However, the incorporation of constant radius and angular displacement in its spiral model causes the exploration strategy to be less effective hence resulting in low accurate solution. Bacteria chemotaxis on the other hand, is the most prominent strategy in bacterial foraging algorithm. However, the incorporation of a constant step-size for the bacteria movement affects the algorithm performance. Defining a large step-size results in faster convergence speed but produces low accuracy while de.ning a small step-size gives high accuracy but produces slower convergence speed. The hybrid algorithms proposed in this paper synergise SDA and bacteria chemotaxis and thus introduce more effective exploration strategy leading to higher accuracy, faster convergence speed and low computation time. The proposed algorithms are tested with several benchmark functions and statistically analysed via nonparametric Friedman and Wilcoxon signed rank tests as well as parametric t-test in comparison to their predecessor algorithms. Moreover, they are used to optimise hybrid Proportional-Derivative-like fuzzy-logic controller for position tracking of a flexible manipulator system. The results show that the proposed algorithms significantly improve both convergence speed as well as fitness accuracy and result in better system response in controlling the flexible manipulator
An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning
Mobile robotic platforms are an indispensable tool for various scientific and
industrial applications. Robots are used to undertake missions whose execution
is constrained by various factors, such as the allocated time or their
remaining energy. Existing solutions for resource constrained multi-robot
sensing mission planning provide optimal plans at a prohibitive computational
complexity for online application [1],[2],[3]. A heuristic approach exists for
an online, resource constrained sensing mission planning for a single vehicle
[4]. This work proposes a Genetic Algorithm (GA) based heuristic for the
Correlated Team Orienteering Problem (CTOP) that is used for planning sensing
and monitoring missions for robotic teams that operate under resource
constraints. The heuristic is compared against optimal Mixed Integer Quadratic
Programming (MIQP) solutions. Results show that the quality of the heuristic
solution is at the worst case equal to the 5% optimal solution. The heuristic
solution proves to be at least 300 times more time efficient in the worst
tested case. The GA heuristic execution required in the worst case less than a
second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and
Automation Letters (RA-L
Search-Based Fairness Testing: An Overview
Artificial Intelligence (AI) has demonstrated remarkable capabilities in
domains such as recruitment, finance, healthcare, and the judiciary. However,
biases in AI systems raise ethical and societal concerns, emphasizing the need
for effective fairness testing methods. This paper reviews current research on
fairness testing, particularly its application through search-based testing.
Our analysis highlights progress and identifies areas of improvement in
addressing AI systems biases. Future research should focus on leveraging
established search-based testing methodologies for fairness testing.Comment: IEEE International Conference on Computing (ICOCO 2023), Langkawi
Island, Malaysia, pp. 89-94, October 202
Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification
In recent years, technology in medicine has shown a significant advance due to artificial intelligence becoming a framework to make accurate medical diagnoses. Models like Multilayer Perceptrons (MLPs) can detect implicit patterns in data, allowing identifying patients conditions that cannot be seen easily. MLPs consist of biased neurons arranged in layers, connected by weighted connections. Their effectiveness depends on finding the optimal weights and biases that reduce the classification error, which is usually done by using the Back Propagation algorithm (BP). But BP has several disadvantages that could provoke the MLP not to learn. Metaheuristics are alternatives to BP that reach high-quality solutions without using many computational resources. In this work, the Cellular Genetic Algorithm (CGA) with a specially designed crossover operator called Damped Crossover (DX), is proposed to optimise weights and biases of the MLP to classify medical data. When compared against state-of-the-art algorithms, the CGA configured with DX obtained the minimal Mean Square Error value in three out of the five considered medical datasets and was the quickest algorithm with four datasets, showing a better balance between time consumed and optimisation performance. Additionally, it is competitive in enhancing classification quality, reaching the best accuracy with two datasets and the second-best accuracy with two of the remaining.Fil: Rojas, Matias Gabriel. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las TecnologÃas de la Información y las Comunicaciones; ArgentinaFil: Olivera, Ana Carolina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las TecnologÃas de la Información y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de IngenierÃa; ArgentinaFil: Vidal, Pablo Javier. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las TecnologÃas de la Información y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de IngenierÃa; Argentin
Evolutionary algorithms for robot path planning, task allocation and collision avoidance in an automated warehouse
Thesis (PhD)--Stellenbosch University, 2022.ENGLISH ABSTRACT: Research with regard to path planning, task allocation and collision avoidance is important
for improving the field of warehouse automation. The dissertation addresses the topic of routing
warehouse picking and binning robots. The purpose of this dissertation is to develop a single
objective and multi-objective algorithm framework that can sequence products to be picked or
binned, allocate the products to robots and optimise the routing through the warehouse. The
sequence of the picking and binning tasks ultimately determines the total time for picking and
binning all of the parts. The objectives of the algorithm framework are to minimise the total
time for travelling as well as the total time idling, given the number of robots available to perform
the picking and binning functions. The algorithm framework incorporates collision avoidance
since the aisle width does not allow two robots to pass each other. The routing problem sets
the foundation for solving the sequencing and allocation problem. The best heuristic from the
routing problem is used as the strategy for routing the robots in the sequencing and allocation
problem. The routing heuristics used to test the framework in this dissertation include the
return heuristic, the s-shape heuristic, the midpoint heuristic and the largest gap heuristic. The
metaheuristic solution strategies for single objective part sequencing and allocating problem
include the covariance matrix adaptation evolution strategy (CMA-ES) algorithm, the genetic
algorithm (GA), the guaranteed convergence particle swarm optimisation (GCPSO) algorithm,
and the self-adaptive differential evolution algorithm with neighbourhood search (SaNSDE). The
evolutionary multi-objective algorithms considered in this dissertation are the non-dominated
sorting genetic algorithm III (NSGA-III), the multi-objective evolutionary algorithm based on
decomposition (MOEAD), the multiple objective particle swarm optimisation (MOPSO), and
the multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES).
Solving the robot routing problem showed that the return routing heuristic outperformed the
s-shape, largest gap and midpoint heuristics with a significant margin. The return heuristic was
thus used for solving the routing of robots in the part sequencing and allocation problem.
The framework was able to create feasible real-world solutions for the part sequencing and
allocation problem. The results from the single objective problem showed that the CMA-ES
algorithm outperformed the other metaheuristics on the part sequencing and allocation problem.
The second best performing metaheuristic was the SaNSDE.
The GA was the third best metaheuristic and the worst performing metaheuristic was the
GCPSO. The multi-objective framework was able to produce feasible trade-off solutions and
MOPSO was shown to be the best EMO algorithm to use for accuracy. If a large spread and
number of Pareto solutions are the most important concern, MOEAD should be used.
The research contributions include the incorporation of collision avoidance in the robot
routing problem when using single and multi-objective algorithms as solution strategies. This
dissertation contributes to the research relating to the performance of metaheuristics and
evolutionary multi-objective algorithms on routing, sequencing, and allocation problems. To
the best of the author’s knowledge, this dissertation is the first where these four metaheuristics
and evolutionary multi-objective algorithms have been tested for solving the robot picking
and binning problem, given that all collisions must be avoided. It is also the first time
that this specific variation of the part sequencing and allocation problem has been solved
using metaheuristics and evolutionary multi-objective algorithms, taking into account that all
collisions must be avoided.AFRIKAANSE OPSOMMING: Navorsing in verband met roete beplanning, part allokasie en botsing vermyding is belangrik
vir die bevordering van die pakhuis automatisering veld. Die verhandeling handel oor die
onderwerp van parte wat gestoor en gehaal moet word en die verkillende parte moet ook
gealokeer word aan ’n spesifieke robot. Die doel van hierdie verhandeling is om ’n enkele
doelwit en ’n multidoelwit algoritme raamwerk te ontwikkel wat parte in ’n volgorde rangskik
en ook die parte aan ’n robot alokeer. Die roete wat die robot moet volg deur die pakhuis
moet ook geoptimeer word om die minste tyd te verg. Die volgorde van die parte bepaal
uiteindelik die totale tyd wat dit neem vir die robot om al die parte te stoor en te gaan
haal. Die doelwitte van die algoritme raamwerk is om die totale reistyd en die totale ledige
tyd te minimeer, gegewe die aantal beskikbare robotte in die sisteem om die stoor en gaan
haal funksies uit te voer. Die algoritme raamwerk bevat botsingsvermyding, aangesien die
gangbreedte van die pakhuis nie toelaat dat twee robotte mekaar kan verbygaan nie. Die roete
probleem lˆe die grondslag vir die oplossing van die volgorde en allokerings probleem. Die beste
heuristiek vir die roete probleem word verder gebruik in die volgorde en allokerings probleem.
Die verskillende roete heuristieke wat in hierdie verhandeling oorweeg was, sluit in die terugkeer
heuristiek, die s-vorm heuristiek, die middelpunt heuristiek en die grootste gaping heuristiek.
Die metaheuristieke vir die volgorde en allokerings probleem sluit die volgende algoritmes in:
die kovariansie matriks aanpassing evolusie algoritme (CMA-ES), die genetiese algoritme (GA),
die gewaarborgde konvergerende deeltjie swermoptimerings (GCPSO) algoritme, en laastens
die selfaanpassende differensi¨ele evolusie algoritme met die teenwoordigheid van buurtsoek
(SaNSDE). Die evolusionêre multidoelwit algoritmes wat oorweeg was vir die volgorde en
allokerings probleem sluit die volgende algoritmes in: die multidoelwit kovariansie matriks
aanpassing evolusie algoritme (MO-CMA-ES), die nie-dominerende sortering genetiese algoritme
III (NSGA-III), die multidoelwit evolusionˆere algoritme gebaseer op ontbinding (MOEAD) en
laastens die multidoelwit deeltjie swermoptimering algoritme (MOPSO)
Oplossings van die robot roete probleem het gewys dat die terugkeer heuristiek die s-vorm,
grootste gaping en middelpunt heuristiek met ’n beduidende marge oortref het. Die terugkeer
heuristiek is dus gebruik vir die oplossing van die roete beplanning van robotte in die volgorde
en allokasie probleem.
Die raamwerk was doeltreffend en die resultate het getoon, vir die enkel doelwit probleem, dat
die CMA-ES algoritme beter gevaar het as die ander metaheuristieke vir die volgorde en allokasie
probleem. Die SaNSDE was die naas beste presterende metaheuristiek. Die GA was die derde
beste metaheuristiek, en die metaheuristiek wat die slegste gevaar het, was die GCPSO. Vir
die multidoelwit probleem het die MOPSO die beste gevaar, as akkuraatheid die belangrikste
doelwit is. As ’n grootter verskeidenheid die belangrikste is, is die MOEAD meer geskik om ’n
oplossing te vind.
Die navorsingsbydraes sluit in dat vermyding van botsings in ag geneem word in die robot
roete probleem. Hierdie verhandeling dra by tot die navorsing oor die oplossing van roete
beplanning, volgorde en allokasie probleme met metaheuristieke. Na die beste van die outeur se
kennis is hierdie die eerste keer dat al vier metaheuristieke getoets was om die robot stoor-en-gaan
haal probleem op te los, onder die kondisie dat alle botsings vermy moet word. Dit is ook die
eerste keer dat hierdie spesifieke variant, enkel-en-multidoelwit probleem van die volgorde en
allokasie van parte met behulp van metaheuristieke en multidoelwit evolusionˆere algoritmes
opgelos was, met die inagneming dat alle botsings vermy moet word.Doctora
A New K means Grey Wolf Algorithm for Engineering Problems
Purpose: The development of metaheuristic algorithms has increased by
researchers to use them extensively in the field of business, science, and
engineering. One of the common metaheuristic optimization algorithms is called
Grey Wolf Optimization (GWO). The algorithm works based on imitation of the
wolves' searching and the process of attacking grey wolves. The main purpose of
this paper to overcome the GWO problem which is trapping into local optima.
Design or Methodology or Approach: In this paper, the K-means clustering
algorithm is used to enhance the performance of the original Grey Wolf
Optimization by dividing the population into different parts. The proposed
algorithm is called K-means clustering Grey Wolf Optimization (KMGWO).
Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To
evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019
benchmark test functions. Results prove that KMGWO is better compared to GWO.
KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization
Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank
in terms of performance. Statistical results proved that KMGWO achieved a
higher significant value compared to the compared algorithms. Also, the KMGWO
is used to solve a pressure vessel design problem and it has outperformed
results.
Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also
compared to cat swarm optimization (CSO), whale optimization algorithm-bat
algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of
performance. Also, the KMGWO is used to solve a classical engineering problem
and it is superiorComment: 15 pages. World Journal of Engineering, 202
Evolutionary algorithm-based analysis of gravitational microlensing lightcurves
A new algorithm developed to perform autonomous fitting of gravitational
microlensing lightcurves is presented. The new algorithm is conceptually
simple, versatile and robust, and parallelises trivially; it combines features
of extant evolutionary algorithms with some novel ones, and fares well on the
problem of fitting binary-lens microlensing lightcurves, as well as on a number
of other difficult optimisation problems. Success rates in excess of 90% are
achieved when fitting synthetic though noisy binary-lens lightcurves, allowing
no more than 20 minutes per fit on a desktop computer; this success rate is
shown to compare very favourably with that of both a conventional (iterated
simplex) algorithm, and a more state-of-the-art, artificial neural
network-based approach. As such, this work provides proof of concept for the
use of an evolutionary algorithm as the basis for real-time, autonomous
modelling of microlensing events. Further work is required to investigate how
the algorithm will fare when faced with more complex and realistic microlensing
modelling problems; it is, however, argued here that the use of parallel
computing platforms, such as inexpensive graphics processing units, should
allow fitting times to be constrained to under an hour, even when dealing with
complicated microlensing models. In any event, it is hoped that this work might
stimulate some interest in evolutionary algorithms, and that the algorithm
described here might prove useful for solving microlensing and/or more general
model-fitting problems.Comment: 14 pages, 3 figures; accepted for publication in MNRA
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