Illuminating meaningful diversity in complex feature spaces through adaptive grid-based genetic algorithms

In many fields there exist problems for which multiple solutions of suitably high performance may be found across distinct regions of the search space. Optimisation of the search towards including these distinct solutions is important not only to understanding these spaces but also to avoiding local optima. This is the goal of a type of genetic algorithms called illumination algorithms. In Chapter 2, we demonstrate the use of an illumination algorithm in the exploration of networks sharing only a given set of structural features (valid networks). This method produces a population of valid networks that are more diverse than those produced using state of the art methods, however, it was found to be too inefficient to be usable in real-world problems. Additionally, setting an appropriate resolution of the search requires some amount of prior knowledge of the space of solutions. Addressing this problem is the focus of Chapter 3, in which we develop three extensions to the method: a) an exact method of mutation whereby only valid networks are explored, b) an adaptive mechanism for setting the resolution of the search, c) a principle for tuning mutations parameters to the search’ s resolution. We show that with these additions our method is able to increase the diversity of solutions found in significantly fewer iterations. Finally, in Chapter 4 we expand our method for use in more general problem spaces. We benchmark it against the state of the art. In all tested landscapes, we show that our method is able to identify more meaningful niches in the spaces in the same number of iterations. We conclude by highlighting the limits of our framework and discuss further directions

Using novelty-biased GA to sample diversity in graphs satisfying constraints

The structure of the network underlying many complex systems, whether artificial or natural, plays a significant role in how these systems operate. As a result, much emphasis has been placed on accurately describing networks using network theoretic metrics. When it comes to generating networks with similar properties, however, the set of available techniques and properties that can be controlled for remains limited. Further, whilst it is becoming clear that some of the metrics currently used to control the generation of such networks are not very prescriptive so that networks could potentially exhibit very different higher-order structure within those constraints, network generating algorithms typically produce fairly contrived networks and lack mechanisms by which to systematically explore the space of network solutions. In this paper, we explore the potential of a multi-objective novelty-biased GA to provide a viable alternative to these algorithms. We believe our results provide the first proof of principle that (i) it is possible to use GAs to generate graphs satisfying set levels of key classical graph theoretic properties and (ii) it is possible to generate diverse solutions within these constraints. The paper is only a preliminary step, however, and we identify key avenues for further development

A genetic algorithm-based approach to mapping the diversity of networks sharing a given degree distribution and global clustering

The structure of a network plays a key role in the outcome of dynamical processes operating on it. Two prevalent network descriptors are the degree distribution and the global clustering. However, when generating networks with a prescribed degree distribution and global clustering, it has been shown that changes in structural properties other than that controlled for are induced and these changes have been found to alter the outcome of spreading processes on the network. This therefore begs the question of our understanding of the potential diversity of networks sharing a given degree distribution and global clustering. As the space of all possible networks is too large to be systematically explored, a heuristic approach is needed. In our genetic algorithm-based approach, networks are encoded by their subgraph counts from a chosen family of subgraphs. Coverage of the space of possible networks is then maximised by focusing the search through optimising the diversity of counts by the Map-Elite algorithm. We provide preliminary evidence of our approach’s ability to sample from the space of possible networks more widely than some state of the art methods

Virtual Special Issue on Catalysis at the U.S. Department of Energy’s National Laboratories

Catalysis research at the U.S. Department of Energy’s (DOE’s) National Laboratories covers a wide range of research topics in heterogeneous catalysis, homogeneous/molecular catalysis, biocatalysis, electrocatalysis, and surface science. Since much of the work at National Laboratories is funded by DOE, the research is largely focused on addressing DOE’s mission to ensure America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. The catalysis research carried out at the DOE National Laboratories ranges from very fundamental catalysis science, funded by DOE’s Office of Basic Energy Sciences (BES), to applied research and development (R&D) in areas such as biomass conversion to fuels and chemicals, fuel cells, and vehicle emission control with primary funding from DOE’s Office of Energy Efficiency and Renewable Energy. National Laboratories are home to many DOE Office of Science national scientific user facilities that provide researchers with the most advanced tools of modern science, including accelerators, colliders, supercomputers, light sources, and neutron sources, as well as facilities for studying the nanoworld and the terrestrial environment. National Laboratory research programs typically feature teams of researchers working closely together, often joining scientists from different disciplines to tackle scientific and technical problems using a variety of tools and techniques available at the DOE national scientific user facilities. Along with collaboration between National Laboratory scientists, interactions with university colleagues are common in National Laboratory catalysis R&D. In some cases, scientists have joint appointments at a university and a National Laboratory. This ACS Catalysis Virtual Special Issue {http://pubs.acs.org/page/accacs/vi/doe-national-labs} was motivated by Christopher Jones and Rhea Williams, who sent out the invitations to all of DOE’s National Laboratories where catalysis research is conducted. All manuscripts submitted went through the standard rigorous peer review required for publication in ACS Catalysis. A total of 29 papers are published in this virtual special issue, which features some of the recent catalysis research at 11 of DOE’s National Laboratories: Ames Laboratory (Ames), Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), National Energy Technology Laboratory (NETL), National Renewable Energy Laboratory (NREL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL), Sandia National Laboratory (SNL), and SLAC National Accelerator Laboratory (SLAC). In this preface, we briefly discuss the history and impact of catalysis research at these particular DOE National Laboratories, where the majority of catalysis research continues to be conducted

Implementations and optimisations of optical Conv2D networks designs

Deep learning for object detection offers the advantage of very low electrical power requirements but with the potential of a very large computation bandwidth due to the ability of Fraunhofer diffraction to perform correlation operations. However, many of the current designs of deep learning networks are not easily implemented in the optical domain. In this paper we develop a modified version of the deep learning library, Keras, that can accurately model optical systems. This allows the discovery of the optimal weights by calculating them on a realistic optical system. Noise sources, speckle models, and calibration errors can be accounted for. The effect of using readily realisable filters such as nematic liquid crystal phase only spatial light modulators is investigated. The effect of multiplexing a number of correlations in order to replicate the Conv2D multiple channel input is assessed. The effect of an optically implementable bias and activation functions are examined and compared to the state-of-the-art software implementations. We show that object recognition can be achieved using spatial light modulator technology and give comparable results to digital implementations

Virtual Special Issue on Catalysis at the U.S. Department of Energy’s National Laboratories

Catalysis research at the U.S. Department of Energy’s (DOE’s) National Laboratories covers a wide range of research topics in heterogeneous catalysis, homogeneous/molecular catalysis, biocatalysis, electrocatalysis, and surface science. Since much of the work at National Laboratories is funded by DOE, the research is largely focused on addressing DOE’s mission to ensure America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. The catalysis research carried out at the DOE National Laboratories ranges from very fundamental catalysis science, funded by DOE’s Office of Basic Energy Sciences (BES), to applied research and development (R&D) in areas such as biomass conversion to fuels and chemicals, fuel cells, and vehicle emission control with primary funding from DOE’s Office of Energy Efficiency and Renewable Energy. National Laboratories are home to many DOE Office of Science national scientific user facilities that provide researchers with the most advanced tools of modern science, including accelerators, colliders, supercomputers, light sources, and neutron sources, as well as facilities for studying the nanoworld and the terrestrial environment. National Laboratory research programs typically feature teams of researchers working closely together, often joining scientists from different disciplines to tackle scientific and technical problems using a variety of tools and techniques available at the DOE national scientific user facilities. Along with collaboration between National Laboratory scientists, interactions with university colleagues are common in National Laboratory catalysis R&D. In some cases, scientists have joint appointments at a university and a National Laboratory. This ACS Catalysis Virtual Special Issue {http://pubs.acs.org/page/accacs/vi/doe-national-labs} was motivated by Christopher Jones and Rhea Williams, who sent out the invitations to all of DOE’s National Laboratories where catalysis research is conducted. All manuscripts submitted went through the standard rigorous peer review required for publication in ACS Catalysis. A total of 29 papers are published in this virtual special issue, which features some of the recent catalysis research at 11 of DOE’s National Laboratories: Ames Laboratory (Ames), Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), National Energy Technology Laboratory (NETL), National Renewable Energy Laboratory (NREL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL), Sandia National Laboratory (SNL), and SLAC National Accelerator Laboratory (SLAC). In this preface, we briefly discuss the history and impact of catalysis research at these particular DOE National Laboratories, where the majority of catalysis research continues to be conducted.This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Catalysis, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see DOI: 10.1021/acscatal.6b00823. Posted with permission.</p