Geometric semantic genetic programming for recursive boolean programs

This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.Geometric Semantic Genetic Programming (GSGP) induces a unimodal fitness landscape for any problem that consists in finding a function fitting given input/output examples. Most of the work around GSGP to date has focused on real-world applications and on improving the originally proposed search operators, rather than on broadening its theoretical framework to new domains. We extend GSGP to recursive programs, a notoriously challenging domain with highly discontinuous fitness landscapes. We focus on programs that map variable-length Boolean lists to Boolean values, and design search operators that are provably efficient in the training phase and attain perfect generalization. Computational experiments complement the theory and demonstrate the superiority of the new operators to the conventional ones. This work provides new insights into the relations between program syntax and semantics, search operators and fitness landscapes, also for more general recursive domains.© 2017 Copyright held by the owner/author(s). Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Thermoelectric phenomena in a quantum dot asymmetrically coupled to external leads

We study thermoelectric phenomena in a system consisting of strongly correlated quantum dot coupled to external leads in the Kondo regime. We calculate linear and nonlinear electrical and thermal conductance and thermopower of the quantum dot and discuss the role of asymmetry in the couplings to external electrodes. In the linear regime electrical and thermal conductances are modified, while thermopower remains unchanged. In the nonlinear regime the Kondo resonance in differential conductance develops at non-zero source-drain voltage, which has important consequences on thermoelectric properties of the system and the thermopower starts to depend on the asymmetry. We also discuss Wiedemann-Franz relation, thermoelectric figure of merit and validity of the Mott formula for thermopower.Comment: 6 pages, 7 figure

Lexicase selection in Learning Classifier Systems

The lexicase parent selection method selects parents by considering performance on individual data points in random order instead of using a fitness function based on an aggregated data accuracy. While the method has demonstrated promise in genetic programming and more recently in genetic algorithms, its applications in other forms of evolutionary machine learning have not been explored. In this paper, we investigate the use of lexicase parent selection in Learning Classifier Systems (LCS) and study its effect on classification problems in a supervised setting. We further introduce a new variant of lexicase selection, called batch-lexicase selection, which allows for the tuning of selection pressure. We compare the two lexicase selection methods with tournament and fitness proportionate selection methods on binary classification problems. We show that batch-lexicase selection results in the creation of more generic rules which is favorable for generalization on future data. We further show that batch-lexicase selection results in better generalization in situations of partial or missing data.Comment: Genetic and Evolutionary Computation Conference, 201

Electron transport across a quantum wire in the presence of electron leakage to a substrate

We investigate electron transport through a mono-atomic wire which is tunnel coupled to two electrodes and also to the underlying substrate. The setup is modeled by a tight-binding Hamiltonian and can be realized with a scanning tunnel microscope (STM). The transmission of the wire is obtained from the corresponding Green's function. If the wire is scanned by the contacting STM tip, the conductance as a function of the tip position exhibits oscillations which may change significantly upon increasing the number of wire atoms. Our numerical studies reveal that the conductance depends strongly on whether or not the substrate electrons are localized. As a further ubiquitous feature, we observe the formation of charge oscillations.Comment: 7 pages, 7 figure

Influence of carbon on spin reorientation processes in Er 2-xRxFe14C (R = Gd, Pr) - Mossbauer and magnetometric studies

The Er2¡xRxFe14C (R=Gd, Pr) polycrystalline compounds have been synthesized and investigated with 57Fe Mössbauer spectroscopy and magnetic measurements. The spin reorientation phenomena were studied extensively by narrow step temperature scanning in the neighborhood of the spin reorientation temperature. Obtained Mössbauer spectra were analyzed using a procedure of simultaneous fitting and the transmission integral approach. Consistent description of Mössbauer spectra were obtained, temperature and composition dependencies of hyperfine interaction parameters and subspectra contributions were derived from fits and the transition temperatures were determined for all the compounds studied. Initial magnetization versus temperature measurements (in zero and non-zero external field) for Er2¡xGdxFe14C compounds allowed to establish the temperature regions of reorientation, change of magnetization value during the transition process. The results obtained with different methods were analyzed and the spin arrangement diagrams were constructed. Data obtained for Er2¡xGdxFe14C were compared with those for Er2¡xGdxFe14B series

Geometric Semantic Grammatical Evolution

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.Geometric Semantic Genetic Programming (GSGP) is a novel form of Genetic Programming (GP), based on a geometric theory of evolutionary algorithms, which directly searches the semantic space of programs. In this chapter, we extend this framework to Grammatical Evolution (GE) and refer to the new method as Geometric Semantic Grammatical Evolution (GSGE). We formally derive new mutation and crossover operators for GE which are guaranteed to see a simple unimodal fitness landscape. This surprising result shows that the GE genotypephenotype mapping does not necessarily imply low genotype-fitness locality. To complement the theory, we present extensive experimental results on three standard domains (Boolean, Arithmetic and Classifier)

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