Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings

While evolutionary algorithms are known to be very successful for a broad range of applications, the algorithm designer is often left with many algorithmic choices, for example, the size of the population, the mutation rates, and the crossover rates of the algorithm. These parameters are known to have a crucial influence on the optimization time, and thus need to be chosen carefully, a task that often requires substantial efforts. Moreover, the optimal parameters can change during the optimization process. It is therefore of great interest to design mechanisms that dynamically choose best-possible parameters. An example for such an update mechanism is the one-fifth success rule for step-size adaption in evolutionary strategies. While in continuous domains this principle is well understood also from a mathematical point of view, no comparable theory is available for problems in discrete domains. In this work we show that the one-fifth success rule can be effective also in discrete settings. We regard the $(1+(\lambda,\lambda))$~GA proposed in [Doerr/Doerr/Ebel: From black-box complexity to designing new genetic algorithms, TCS 2015]. We prove that if its population size is chosen according to the one-fifth success rule then the expected optimization time on \textsc{OneMax} is linear. This is better than what \emph{any} static population size $\lambda$ can achieve and is asymptotically optimal also among all adaptive parameter choices.Comment: This is the full version of a paper that is to appear at GECCO 201

Improving Building Energy Efficiency through Measurement of Building Physics Properties Using Dynamic Heating Tests

© 2019 the author. Licensee MDPI, Basel, Switzerland.Buildings contribute to nearly 30% of global carbon dioxide emissions, making a significant impact on climate change. Despite advanced design methods, such as those based on dynamic simulation tools, a significant discrepancy exists between designed and actual performance. This so-called performance gap occurs as a result of many factors, including the discrepancies between theoretical properties of building materials and properties of the same materials in buildings in use, reflected in the physics properties of the entire building. There are several different ways in which building physics properties and the underlying properties of materials can be established: a co-heating test, which measures the overall heat loss coefficient of the building; a dynamic heating test, which, in addition to the overall heat loss coefficient, also measures the effective thermal capacitance and the time constant of the building; and a simulation of the dynamic heating test with a calibrated simulation model, which establishes the same three properties in a non-disruptive way in comparison with the actual physical tests. This article introduces a method of measuring building physics properties through actual and simulated dynamic heating tests. It gives insights into the properties of building materials in use and it documents significant discrepancies between theoretical and measured properties. It introduces a quality assurance method for building construction and retrofit projects, and it explains the application of results on energy efficiency improvements in building design and control. It calls for re-examination of material properties data and for increased safety margins in order to make significant improvements in building energy efficiency.Peer reviewedFinal Published versio

No entailing laws, but enablement in the evolution of the biosphere

Biological evolution is a complex blend of ever changing structural stability, variability and emergence of new phenotypes, niches, ecosystems. We wish to argue that the evolution of life marks the end of a physics world view of law entailed dynamics. Our considerations depend upon discussing the variability of the very "contexts of life": the interactions between organisms, biological niches and ecosystems. These are ever changing, intrinsically indeterminate and even unprestatable: we do not know ahead of time the "niches" which constitute the boundary conditions on selection. More generally, by the mathematical unprestatability of the "phase space" (space of possibilities), no laws of motion can be formulated for evolution. We call this radical emergence, from life to life. The purpose of this paper is the integration of variation and diversity in a sound conceptual frame and situate unpredictability at a novel theoretical level, that of the very phase space. Our argument will be carried on in close comparisons with physics and the mathematical constructions of phase spaces in that discipline. The role of (theoretical) symmetries as invariant preserving transformations will allow us to understand the nature of physical phase spaces and to stress the differences required for a sound biological theoretizing. In this frame, we discuss the novel notion of "enablement". This will restrict causal analyses to differential cases (a difference that causes a difference). Mutations or other causal differences will allow us to stress that "non conservation principles" are at the core of evolution, in contrast to physical dynamics, largely based on conservation principles as symmetries. Critical transitions, the main locus of symmetry changes in physics, will be discussed, and lead to "extended criticality" as a conceptual frame for a better understanding of the living state of matter

Evolutionary dynamics, intrinsic noise and cycles of co-operation

We use analytical techniques based on an expansion in the inverse system size to study the stochastic evolutionary dynamics of finite populations of players interacting in a repeated prisoner's dilemma game. We show that a mechanism of amplification of demographic noise can give rise to coherent oscillations in parameter regimes where deterministic descriptions converge to fixed points with complex eigenvalues. These quasi-cycles between co-operation and defection have previously been observed in computer simulations; here we provide a systematic and comprehensive analytical characterization of their properties. We are able to predict their power spectra as a function of the mutation rate and other model parameters, and to compare the relative magnitude of the cycles induced by different types of underlying microscopic dynamics. We also extend our analysis to the iterated prisoner's dilemma game with a win-stay lose-shift strategy, appropriate in situations where players are subject to errors of the trembling-hand type.Comment: 14 pages, 12 figures, accepted for publication by Phys. Rev.

Subtree power analysis finds optimal species for comparative genomics

Sequence comparison across multiple organisms aids in the detection of regions under selection. However, resource limitations require a prioritization of genomes to be sequenced. This prioritization should be grounded in two considerations: the lineal scope encompassing the biological phenomena of interest, and the optimal species within that scope for detecting functional elements. We introduce a statistical framework for optimal species subset selection, based on maximizing power to detect conserved sites. In a study of vertebrate species, we show that the optimal species subset is not in general the most evolutionarily diverged subset. Our results suggest that marsupials are prime sequencing candidates.Comment: 16 pages, 3 figures, 3 table