In the quest for Energy Efficient Genetic Algorithms

Although usually quality of solutions and running time are the main features of algorithms, recently a new trend in computer science tries to contextualize these features under a new perspective: power consumption. This paper presents a preliminary analysis of the standard genetic algorithm, using two well-known benchmark problems, considering power consumption when battery-powered devices are used to run them. Results show that some of the main parameters of the algorithm has an impact on instantaneous energy consumption -that departs from the expected behavior, and therefore on the amount of energy required to run the algorithm. Although we are still far from finding the way to design energy-efficient EAs, we think the results open up a new perspective that will allow us to achieve this goal in the future. 

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Local search in speciation-based bloat control for genetic programming

This work presents a unique genetic programming (GP) approach that integrates a numerical local search method and a bloat-control mechanism to address some of the main issues with traditional GP. The former provides a directed search operator to work in conjunction with standard syntax operators that perform more exploration in design space, while the latter controls code growth by maintaining program diversity through speciation. The system can produce highly parsimonious solutions, thus reducing the cost of performing the local optimization process. The proposal is extensively evaluated using real-world problems from diverse domains, and the behavior of the search is analyzed from several different perspectives, including how species evolve, the effect of the local search process and the interpretability of the results. Results show that the proposed approach compares favorably with a standard approach, and that the hybrid algorithm can be used as a viable alternative for solving real-world symbolic regression problems.

DOI  https://doi.org/10.1007/s10710-019-09351-7

to be, or not to be: that is the recursive question.

This paper discusses the opportunity of Functional Programming for making students aware about data dependencies and their implications when using parallel and distributed computing infrastructures. Although other programming methodologies, such as Object Oriented Programming (OOP) are usually preferred to be taught at computer science degrees, the problem is that the sequential programming approach is inherent to the model, and once students have entered the framework, it is not easy for them to learn modern parallel programming models. Thus, the methodology learned may act as a straitjacket, preventing students from taking advantage of the parallel architectures widely available. The idea presented here relies on choosing Functional Programming as the methodology to be learned first. Moreover, when any selected language that embodies the functional model is shown to students, we propose to forbid loops, similarly as how go-to sentences are classically forbidden in high level programming languages, or global variables are forbidden to avoid side effects. Students must thus resort instead to recursive functions if data dependencies are present and a sequential order of operations is required, or to map functions when no dependencies exist. This way, students naturally develop the skill to automatically write parallel code within the functional programming context, and then the map/reduce model can be easily exploited in any context when parallel and distributed infrastructures are available. We describe preliminary results obtained when the model has been successfully tested with a group of middle school students.

DOI: 10.1109/EDUCON.2019.8725191

Clustering analysis of FDG-PET imaging in primary progressive aphasia

Background: Primary progressive aphasia (PPA) is a clinical syndrome characterized by the neurodegeneration of language brain systems. Three main clinical forms (non-fluent, semantic, and logopenic PPA) have been recognized, but applicability of the classification and the capacity to predict the underlying pathology is controversial. We aimed to study FDG-PET imaging data in a large consecutive case series of patients with PPA to cluster them into different subtypes according to regional brain metabolism. Methods: 122 FDG-PET imaging studies belonging to 91 PPA patients and 28 healthy controls were included. We developed a hierarchical agglomerative cluster analysis with Ward's linkage method, an unsupervised clustering algorithm. We conducted voxel-based brain mapping analysis to evaluate the patterns of hypometabolism of each identified cluster. Results: Cluster analysis confirmed the three current PPA variants, but the optimal number of clusters according to Davies-Bouldin index was 6 subtypes of PPA. This classification resulted from splitting non-fluent variant into three subtypes, while logopenic PPA was split into two subtypes. Voxel-brain mapping analysis displayed different patterns of hypometabolism for each PPA group. New subtypes also showed a different clinical course and were predictive of amyloid imaging results. Conclusion: Our study found that there are more than the three already recognized subtypes of PPA. These new subtypes were more predictive of clinical course and showed different neuroimaging patterns. Our results support the usefulness of FDG-PET in evaluating PPA, and the applicability of computational methods in the analysis of brain metabolism for improving the classification of neurodegenerative disorders.

DOI: 10.3389/fnagi.2018.00230