Novelty-assisted Interactive Evolution Of Control Behaviors

The field of evolutionary computation is inspired by the achievements of natural evolution, in which there is no final objective. Yet the pursuit of objectives is ubiquitous in simulated evolution because evolutionary algorithms that can consistently achieve established benchmarks are lauded as successful, thus reinforcing this paradigm. A significant problem is that such objective approaches assume that intermediate stepping stones will increasingly resemble the final objective when in fact they often do not. The consequence is that while solutions may exist, searching for such objectives may not discover them. This problem with objectives is demonstrated through an experiment in this dissertation that compares how images discovered serendipitously during interactive evolution in an online system called Picbreeder cannot be rediscovered when they become the final objective of the very same algorithm that originally evolved them. This negative result demonstrates that pursuing an objective limits evolution by selecting offspring only based on the final objective. Furthermore, even when high fitness is achieved, the experimental results suggest that the resulting solutions are typically brittle, piecewise representations that only perform well by exploiting idiosyncratic features in the target. In response to this problem, the dissertation next highlights the importance of leveraging human insight during search as an alternative to articulating explicit objectives. In particular, a new approach called novelty-assisted interactive evolutionary computation (NA-IEC) combines human intuition with a method called novelty search for the first time to facilitate the serendipitous discovery of agent behaviors. iii In this approach, the human user directs evolution by selecting what is interesting from the on-screen population of behaviors. However, unlike in typical IEC, the user can then request that the next generation be filled with novel descendants, as opposed to only the direct descendants of typical IEC. The result of such an approach, unconstrained by a priori objectives, is that it traverses key stepping stones that ultimately accumulate meaningful domain knowledge. To establishes this new evolutionary approach based on the serendipitous discovery of key stepping stones during evolution, this dissertation consists of four key contributions: (1) The first contribution establishes the deleterious effects of a priori objectives on evolution. The second (2) introduces the NA-IEC approach as an alternative to traditional objective-based approaches. The third (3) is a proof-of-concept that demonstrates how combining human insight with novelty search finds solutions significantly faster and at lower genomic complexities than fully-automated processes, including pure novelty search, suggesting an important role for human users in the search for solutions. Finally, (4) the NA-IEC approach is applied in a challenge domain wherein leveraging human intuition and domain knowledge accelerates the evolution of solutions for the nontrivial octopus-arm control task. The culmination of these contributions demonstrates the importance of incorporating human insights into simulated evolution as a means to discovering better solutions more rapidly than traditional approaches

Cancer evolution: mathematical models and computational inference.

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.FM would like to acknowledge the support of The University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited.This is the final published version. It first appeared at http://sysbio.oxfordjournals.org/content/early/2014/10/07/sysbio.syu081.short?rss=1

Ancestral sequence reconstruction as an accessible tool for the engineering of biocatalyst stability

Synthetic biology is the engineering of life to imbue non-natural functionality. As such, synthetic biology has considerable commercial potential, where synthetic metabolic pathways are utilised to convert low value substrates into high value products. High temperature biocatalysis offers several system-level benefits to synthetic biology, including increased dilution of substrate, increased reaction rates and decreased contamination risk. However, the current gamut of tools available for the engineering of thermostable proteins are either expensive, unreliable, or poorly understood, meaning their adoption into synthetic biology workflows is treacherous. This thesis focuses on the development of an accessible tool for the engineering of protein thermostability, based on the evolutionary biology tool ancestral sequence reconstruction (ASR). ASR allows researchers to walk back in time along the branches of a phylogeny and predict the most likely representation of a protein family’s ancestral state. It also has simple input requirements, and its output proteins are often observed to be thermostable, making ASR tractable to protein engineering. Chapter 2 explores the applicability of multiple ASR methods to the engineering of a carboxylic acid reductase (CAR) biocatalyst. Despite the family emerging only 500 million years ago, ancestors presented considerable improvements in thermostability over their modern counterparts. We proceed to thoroughly characterise the ancestral enzymes for their inclusion into the CAR biocatalytic toolbox. Chapter 3 explores why ASR derived proteins may be thermostable despite a mesophilic history. An in silico toolbox for tracking models of protein stability over simulated evolutionary time at the sequence, protein and population level is built. We provide considerable evidence that the sequence alignments of simulated protein families that evolved at marginal stability are saturated with stabilising residues. ASR therefore derives sequences from a dataset biased toward stabilisation. Importantly, while ASR is accessible, it still requires a steep learning curve based on its requirements of phylogenetic expertise. In chapter 4, we utilise the evolutionary model produced in chapter 3 to develop a highly simplified and accessible ASR protocol. This protocol was then applied to engineer CAR enzymes that displayed dramatic increases in thermostability compared to both modern CARs and the thermostable AncCARs presented in chapter 2

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field