161 research outputs found
On Meme Self-Adaptation in Spatially-Structured Multimemetic Algorithms
NMA 2014Multimemetic algorithms (MMAs) are memetic algorithms that explicitly exploit the evolution of memes, i.e., non-genetic expressions of problem-solving strategies. We consider a class of MMAs in which these memes are rewriting rules whose length can be fixed during the run of the algorithm or self-adapt during the search process. We analyze this self-adaptation in the context of spatially-structured MMAs, namely MMAs in which the population is endowed with a certain topology to which interactions (from the point of view of selection and variation operators) are constrained. For the problems considered, it is shown that panmictic (i.e., non-structured) MMAs are more sensitive to this self-adaptation, and that using variable-length memes seems to be a robust strategy throughout different population structures.This work is partially supported by MICINN project ANYSELF (TIN2011-28627-C04-01), by
Junta de Andaluía project DNEMESIS (P10-TIC-6083) and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech
A self-parametrization framework for meta-heuristics
Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are time-consuming, requiring considerable computational effort and they do not take advantage of the meta-heuristics that they parameterize. In order to approach the parameterization of meta-heuristics, in this paper, a self-parameterization framework is proposed. It will automatize the parameterization as an optimization problem, precluding the user from spending too much time on parameterization. The model will automate the parameterization through two meta-heuristics: A meta-heuristic of the solution space and one of the parameter space. To analyze the performance of the framework, a self-parameterization prototype was implemented. The prototype was compared and analyzed in a SP (scheduling problem) and in the TSP (traveling salesman problem). In the SP, the prototype found better solutions than those of the manually parameterized meta-heuristics, although the differences were not statistically significant. In the TSP, the self-parameterization prototype was more effective than the manually parameterized meta-heuristics, this time with statistically significant differences.This work was supported by national funds through the FCT - Fundação para a Ciência e
Tecnologia through the R&D Units Project Scopes: UIDB/00319/2020, and EXPL/EME-SIS/1224/2021
Computational complexity of evolutionary algorithms, hybridizations, and swarm intelligence
Bio-inspired randomized search heuristics such as evolutionary algorithms, hybridizations
with local search, and swarm intelligence are very popular among practitioners
as they can be applied in case the problem is not well understood or when there is
not enough knowledge, time, or expertise to design problem-specific algorithms. Evolutionary
algorithms simulate the natural evolution of species by iteratively applying
evolutionary operators such as mutation, recombination, and selection to a set of solutions
for a given problem. A recent trend is to hybridize evolutionary algorithms with
local search to refine newly constructed solutions by hill climbing. Swarm intelligence
comprises ant colony optimization as well as particle swarm optimization. These modern
search paradigms rely on the collective intelligence of many single agents to find good
solutions for the problem at hand. Many empirical studies demonstrate the usefulness
of these heuristics for a large variety of problems, but a thorough understanding is still
far away.
We regard these algorithms from the perspective of theoretical computer science and
analyze the random time these heuristics need to optimize pseudo-Boolean problems.
This is done in a mathematically rigorous sense, using tools known from the analysis of
randomized algorithms, and it leads to asymptotic bounds on their computational complexity.
This approach has been followed successfully for evolutionary algorithms, but
the theory of hybrid algorithms and swarm intelligence is still in its very infancy. Our
results shed light on the asymptotic performance of these heuristics, increase our understanding
of their dynamic behavior, and contribute to a rigorous theoretical foundation
of randomized search heuristics
SupRB: A Supervised Rule-based Learning System for Continuous Problems
We propose the SupRB learning system, a new Pittsburgh-style learning
classifier system (LCS) for supervised learning on multi-dimensional continuous
decision problems. SupRB learns an approximation of a quality function from
examples (consisting of situations, choices and associated qualities) and is
then able to make an optimal choice as well as predict the quality of a choice
in a given situation. One area of application for SupRB is parametrization of
industrial machinery. In this field, acceptance of the recommendations of
machine learning systems is highly reliant on operators' trust. While an
essential and much-researched ingredient for that trust is prediction quality,
it seems that this alone is not enough. At least as important is a
human-understandable explanation of the reasoning behind a recommendation.
While many state-of-the-art methods such as artificial neural networks fall
short of this, LCSs such as SupRB provide human-readable rules that can be
understood very easily. The prevalent LCSs are not directly applicable to this
problem as they lack support for continuous choices. This paper lays the
foundations for SupRB and shows its general applicability on a simplified model
of an additive manufacturing problem.Comment: Submitted to the Genetic and Evolutionary Computation Conference 2020
(GECCO 2020
Robust Motion and Distortion Correction of Diffusion-Weighted MR Images
Effective image-based correction of motion and other acquisition artifacts became an essential step in diffusion-weighted Magnetic Resonance Imaging (MRI) analysis as the micro-structural tissue analysis advances towards higher-order models. These come with increasing demands on the number of acquired images and the diffusion strength (b-value) yielding lower signal-to-noise ratios (SNR) and a higher susceptibility to artifacts. These conditions, however, render the current image-based correction schemes, which act retrospectively on the acquired images through pairwise registration, more and more ineffective. Following the hypothesis, that a more consequent exploitation of the different intensity relationships between the volumes would reduce registration outliers, a novel correction scheme based on memetic search is proposed. This scheme allows
for incorporating all single image metrics into a multi-objective optimization approach. To allow a quantitative evaluation of registration precision, realistic synthetic data are constructed by extending a diffusion MRI simulation framework by motion and eddy-currents-caused artifacts. The increased robustness and efficacy of the multi-objective registration method is demonstrated on the synthetic as well as in-vivo datasets at different levels of motion and other acquisition artifacts. In contrast to the state-of-the-art methods, the average target registration error (TRE) remained below the single voxel size also at high b-values (3000 s.mm-2) and low signal-to-noise ratio in the moderately artifacted datasets. In the more severely artifacted data, the multi-objective method was able to eliminate most of the registration outliers of the state-of-the-art
methods, yielding an average TRE below the double voxel size. In the in-vivo data, the increased precision manifested itself in the scalar measures as well as the fiber orientation derived from the higher-order Neurite Orientation Dispersion and Density Imaging (NODDI) model. For the neuronal fiber tracts reconstructed on the data after correction, the proposed method most closely resembled the ground-truth. The proposed multi-objective method has not only impact on the evaluation of higher-order diffusion models as well as fiber tractography and connectomics, but could also find application to challenging image registration problems in general
On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation
Understanding which function classes are easy and which are hard for a given algorithm is a fundamental question for the analysis and design of bio-inspired search heuristics. A natural starting point is to consider the easiest and hardest functions for an algorithm. For the (1+1) EA using standard bit mutation (SBM) it is well known that OneMax is an easiest function with unique optimum while Trap is a hardest. In this paper we extend the analysis of easiest function classes to the contiguous somatic hypermutation (CHM) operator used in artificial immune systems. We define a function MinBlocks and prove that it is an easiest function for the (1+1) EA using CHM, presenting both a runtime and a fixed budget analysis. Since MinBlocks is, up to a factor of 2, a hardest function for standard bit mutations, we consider the effects of combining both operators into a hybrid algorithm. We rigorously prove that by combining the advantages of k operators, several hybrid algorithmic schemes have optimal asymptotic performance on the easiest functions for each individual operator. In particular, the hybrid algorithms using CHM and SBM have optimal asymptotic performance on both OneMax and MinBlocks. We then investigate easiest functions for hybrid schemes and show that an easiest function for an hybrid algorithm is not just a trivial weighted combination of the respective easiest functions for each operator.publishersversionPeer reviewe
FDR2-BD: A fast data reduction recommendation tool for tabular big data classification problems
In this paper, a methodological data condensation approach for reducing tabular big datasets in classification problems is presented, named FDR2-BD. The key of our proposal is to analyze data in a dual way (vertical and horizontal), so as to provide a smart combination between feature selection to generate dense clusters of data and uniform sampling reduction to keep only a few representative samples from each problem area. Its main advantage is allowing the model’s predictive quality to be kept in a range determined by a user’s threshold. Its robustness is built on a hyper-parametrization process, in which all data are taken into consideration by following a k-fold procedure. Another significant capability is being fast and scalable by using fully optimized parallel operations provided by Apache Spark. An extensive experimental study is performed over 25 big datasets with different characteristics. In most cases, the obtained reduction percentages are above 95%, thus outperforming state-of-the-art solutions such as FCNN_MR that barely reach 70%. The most promising outcome is maintaining the representativeness of the original data information, with quality prediction values around 1% of the baseline.Fil: Basgall, María José. Universidad de Granada; España. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Naiouf, Ricardo Marcelo. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; ArgentinaFil: Fernández, Alberto. Universidad de Granada; Españ
Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems
Memetic algorithms are popular hybrid search heuristics that integrate
local search into the search process of an evolutionary algorithm
in order to combine the advantages of rapid exploitation and global
optimisation. However, these algorithms are not well understood and
the field is lacking a solid theoretical foundation that explains when
and why memetic algorithms are effective.
We provide a rigorous runtime analysis of a simple memetic algorithm,
the (1+1) MA, on the Hurdle problem class, a landscape class
of tuneable difficulty that shows a “big valley structure”, a characteristic
feature of many hard problems from combinatorial optimisation.
The only parameter of this class is the hurdle width w, which describes
the length of fitness valleys that have to be overcome. We show
that the (1+1) EA requires Θ(n
w) expected function evaluations to
find the optimum, whereas the (1+1) MA with best-improvement and
first-improvement local search can find the optimum in Θ(n
2 +n
3/w2
)
and Θ(n
3/w2
) function evaluations, respectively. Surprisingly, while
increasing the hurdle width makes the problem harder for evolutionary
algorithms, the problem becomes easier for memetic algorithms.
We discuss how these findings can explain and illustrate the success of
memetic algorithms for problems with big valley structures
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