Biomorpher: interactive evolution for parametric design

Combining graph-based parametric design with metaheuristic solvers has to date focussed solely on performance based criteria and solving clearly defined objectives. In this paper, we outline a new method for combining a parametric modelling environment with an interactive Cluster-Orientated Genetic Algorithm (COGA). In addition to performance criteria, evolutionary design exploration can be guided through choice alone, with user motivation that cannot be easily defined. As well as numeric parameters forming a genotype, the evolution of whole parametric definitions is discussed through the use of genetic programming. Visualisation techniques that enable mixing small populations for interactive evolution with large populations for performance-based optimisation are discussed, with examples from both academia and industry showing a wide range of applications

Ecosystem-inspired enterprise modelling framework for collaborative and networked manufacturing systems

Rapid changes in the open manufacturing environment are imminent due to the increase of customer demand, global competition, and digital fusion. This has exponentially increased both complexity and uncertainty in the manufacturing landscape, creating serious challenges for competitive enterprises. For enterprises to remain competitive, analysing manufacturing activities and designing systems to address emergent needs, in a timely and efficient manner, is understood to be crucial. However, existing analysis and design approaches adopt a narrow diagnostic focus on either managerial or engineering aspects and neglect to consider the holistic complex behaviour of enterprises in a collaborative manufacturing network (CMN). It has been suggested that reflecting upon ecosystem theory may bring a better understanding of how to analyse the CMN. The research presented in this paper draws on a theoretical discussion with aim to demonstrate a facilitating approach to those analysis and design tasks. This approach was later operationalised using enterprise modelling (EM) techniques in a novel, developed framework that enhanced systematic analysis, design, and business-IT alignment. It is expected that this research view is opening a new field of investigation

Enabling Normalized Systems in Practice – Exploring a Modeling Approach

Contemporary organizations are required to adapt to a changing environment in an agile way, which is often deemed very challenging. Normalized Systems (NS) theory attempts to build highly evolvable software systems by using systems theory as its theoretical underpinning. A modeling method which supports the identification of the NS elements, required for building NS sofware in practice, is currently missing. Therefore, the paper introduces an approach for creating both data models and processing models in the context of NS, as well as their integration. It is discussed how these models can be taken as the input for the actual creation of evolutionary prototypes by using an earlier developed supporting tool. The modeling approach and its suitability for feeding the tool are evaluated to discover their current strengths and weaknesses

From evolutionary computation to the evolution of things

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems

Managing technical debt: prioritising and quantifying architectural smells

Architectural smells zijn een beruchte schadelijke vorm van ATD die verwijst naar schendingen van bekende ontwerpprincipes die resulteren in ongewenste afhankelijkheden, te grote omvang en overmatige koppeling. Architectural smells hebben een negatieve invloed op de onderhoudbaarheid en evolueerbaarheid van een systeem en maken het moeilijker om wijzigingen aan te brengen en nieuwe functionaliteit toe te voegen. Onderzoekers hebben de afgelopen jaren verschillende soorten architectuurgeuren geïdentificeerd, beschreven en gecategoriseerd. Vervolgens werden verschillende onderzoekstools ontwikkeld om dergelijke geuren automatisch te detecteren op basis van de bron artefacten van een systeem. Vanuit het oogpunt van de praktijk is identificatie alleen niet voldoende om de door architectuurgeuren ontstane technische schuld goed te kunnen beheren. Om de bedreiging die architectuurgeuren vormen voor de onderhoudbaarheid van een systeem goed aan te pakken, hebben praktijkmensen ook ondersteuning nodig voor de prioritering, kwantificering, terugbetaling en monitoring. Helaas is de literatuur over dit onderwerp onvolledig, en ontbreekt de instrumentele ondersteuning voor deze specifieke activiteiten

Meta-parametric design: Developing a computational approach for early stage collaborative practice

Computational design is the study of how programmable computers can be integrated into the process of design. It is not simply the use of pre-compiled computer aided design software that aims to replicate the drawing board, but rather the development of computer algorithms as an integral part of the design process. Programmable machines have begun to challenge traditional modes of thinking in architecture and engineering, placing further emphasis on process ahead of the final result. Just as Darwin and Wallace had to think beyond form and inquire into the development of biological organisms to understand evolution, so computational methods enable us to rethink how we approach the design process itself. The subject is broad and multidisciplinary, with influences from design, computer science, mathematics, biology and engineering. This thesis begins similarly wide in its scope, addressing both the technological aspects of computational design and its application on several case study projects in professional practice. By learning through participant observation in combination with secondary research, it is found that design teams can be most effective at the early stage of projects by engaging with the additional complexity this entails. At this concept stage, computational tools such as parametric models are found to have insufficient flexibility for wide design exploration. In response, an approach called Meta-Parametric Design is proposed, inspired by developments in genetic programming (GP). By moving to a higher level of abstraction as computational designers, a Meta-Parametric approach is able to adapt to changing constraints and requirements whilst maintaining an explicit record of process for collaborative working

Modelling evolution of genome size in prokaryotes in response to changes in their abiotic environment

The size of the genomes of known free-living prokaryotes varies from � 1:3 Mbp to � 13 Mbp. This thesis proposes a possible explanation of this variation due to variability of the physical conditions of the environment. In a stable environment, competition for the resource becomes the main force of selection and smaller (thus cheaper) genomes are favoured. In more variable conditions larger genomes will be preferred, as they have a wider range of response to a less predictable environment. An agent-based model (ABM) of genome evolution in an free-living prokaryotic population has been proposed. Using the classic Hutchinson niche space model, a gene was defined as a Gaussian function over a corresponding niche dimension. The cell can have more than one gene along a given dimension, and the envelope of all the corresponding responses is considered a full description of a cell’s phenotype over that dimension. Gene deletion, gene duplication, and modifying mutations are permitted during reproduction, so the number of genes and their phenotypic effect (height and position of the Gaussian envelope) are free to evolve. The surface under the curve is fixed to prevent ‘supergenes’ from occurring. Change of the environmental conditions is simulated as a bounded random walk with a varying length of the step (a parameter representing variability of the environment). Using this approach, the model is able to reproduce the phenomenon of genome streamlining in more stable environments (analogical to e.g. oligotrophic gyre regions of the ocean) and genome complexification in variable environments. Horizontal gene transfer (HGT) was also introduced, but was found to act in a similar manner as gene duplication and shown no important contribution to the speed of evolution and the adaptive potential of the population

Meta-parametric design

Parametric modelling software often maintains an explicit history of design development in the form of a graph. However, as the graph increases in complexity it quickly becomes inflexible and unsuitable for exploring a wide design space. By contrast, implicit low-level rule systems can offer wide design exploration due to their lack of structure, but often act as black boxes to human observers with only initial conditions and final designs cognisable. In response to these two extremes, the authors propose a new approach called Meta-Parametric Design, combining graph-based parametric modelling with genetic programming. The advantages of this approach are demonstrated using two real case-study projects that widen design exploration whilst maintaining the benefits of a graph representation