Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions

Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach

Many algorithms in workflow scheduling and resource provisioning rely on the performance estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the execution of tasks and predicting their runtime accurately, therefore, becomes an essential part of any Workflow Management System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS) platforms that use clouds for deploying scientific workflows, task runtime prediction becomes more challenging because it requires the processing of a significant amount of data in a near real-time scenario while dealing with the performance variability of cloud resources. Hence, relying on methods such as profiling tasks' execution data using basic statistical description (e.g., mean, standard deviation) or batch offline regression techniques to estimate the runtime may not be suitable for such environments. In this paper, we propose an online incremental learning approach to predict the runtime of tasks in scientific workflows in clouds. To improve the performance of the predictions, we harness fine-grained resources monitoring data in the form of time-series records of CPU utilization, memory usage, and I/O activities that are reflecting the unique characteristics of a task's execution. We compare our solution to a state-of-the-art approach that exploits the resources monitoring data based on regression machine learning technique. From our experiments, the proposed strategy improves the performance, in terms of the error, up to 29.89%, compared to the state-of-the-art solutions.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM International Conference on Utility and Cloud Computin

A theoretical framework for Evolutionary Economic Geography: Industrial dynamics and urban growth as a branching process

We propose a framework that specifies the process of economic development as an evolutionary branching process of product innovations. Each product innovation provides a growth opportunity for an existing firm or a new firm, and for an existing city or a new city. One can then obtain both firm size and city size distributions as two aggregates resulting from a single evolutionary process. Gains from variety at the firm level (economies of scope) and the urban level (Jacobs externalities) provide the central feedback mechanism in economic development generating strong path dependencies in the spatial concentration of industries and the specialisation of cities. Gains from size are also expected, yet these are ultimately bounded by increasing wages. The contribution of our framework lies in providing a micro-foundation of economic geography in terms of the interplay between industrial dynamics and urban growth. The framework is sufficiently general to investigate systematically a number of stylised facts in economic geography, while at the same time it is sufficiently flexible to be extended such as to become applicable in more specific micro-contexts. A number of extensions related to the concepts of knowledge spillover and lock-in, are also discussed.evolutionary economic geography, urban growth, firm growth, Zipf, branching, innovation

Outlining the distinguishing characteristics of an evolutionary theory of innovation

This paper discusses notions of theory in relation to evolutionary understandings of innovation. It starts by empirically demonstrating the relevance of evolutionary perspectives – broadly defined – for understanding the “basics of what’s going on” in the economic sphere when it comes to innovation. It continues to argue and show that appreciative evolutionary understandings of innovation are connected to the Darwinian processes of variation, selection and retention in the theoretical “high range”. Multilevel theorizing, where researchers move between different levels and degrees of abstraction is therefore a key feature of an evolutionary theory of innovation. The paper ends by identifying puzzles and research challenges that evolutionary reasoning with respect to innovation need to address.Innovation, evolutionary theory.

A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers