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A Generative Evolutionary Design Method

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Abstract

This paper describes a generative evolutionary design method, called the ‘schema method’, which requires the design team to participate in the programming of generative and evolutionary rules and representations. The method consists of two phases: in the first phase, the design team develops and encodes the essential and identifiable character of the designs to be generated and evolved; in the second phase, the design team uses an evolutionary system to generate and evolve designs that incorporate this character. This method is based on a previous generative evolutionary design method, developed by John Frazer from the late 1960s onwards, called the ‘concept seeding method’.

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... It is advocated that this approach could enhance the system's capabilities by allowing the generation of complex forms with various details and layouts that would not be possible without using such a system. Several researchers have highlighted the benefits of using evolutionary design (Frazer, 2002;von Buelow, 2007;Janssen, 2006;Narahara et al., 2006). In addition, architectural design has benefited from the application of generative algorithm by adopting five different techniques: genetic algorithm, cellular automata, L-systems, swarm intelligence and shape grammars (Janssen, 2006). ...
... Several researchers have highlighted the benefits of using evolutionary design (Frazer, 2002;von Buelow, 2007;Janssen, 2006;Narahara et al., 2006). In addition, architectural design has benefited from the application of generative algorithm by adopting five different techniques: genetic algorithm, cellular automata, L-systems, swarm intelligence and shape grammars (Janssen, 2006). Indicative examples are presented in Table 3. ...
... The evolutionary design method uses evolutionary software systems (genetic algorithm) in order to enhance designers' ability during the design process. Evolutionary design is broadly recognised by the parametric evolutionary design and generative evolutionary design (Janssen, 2006). ...
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Conventional Computer Aided Design tools lack intuitivity for being used in conceptual architectural design process. This paper identifies the impact of using a haptic based VR 3D sketching interface for integrating novice designers’ cognitions and actions to improve design creativity. This study employs protocol analysis for comparing the collective cognitive and collaborative design protocols of three pairs of novice architectural designers in both 3D and manual sketching sessions. Results show that the simple and tangible haptic based design interface improved designers’ cognitive and collaborative activities. These improvements also increased their engagement with ‘problem-space’ and ’solution-space’ that led towards more artefact maturity. Research findings from this study can help the development of cutting-edge haptic-based collaborative virtual environments in architectural education and associated professions.
... It is advocated that this approach could enhance the system's capabilities by allowing the generation of complex forms with various details and layouts that would not be possible without using such a system. Several researchers have highlighted the benefits of using evolutionary design (Frazer, 2002;von Buelow, 2007;Janssen, 2006;Narahara et al., 2006). In addition, architectural design has benefited from the application of generative algorithm by adopting five different techniques: genetic algorithm, cellular automata, L-systems, swarm intelligence and shape grammars (Janssen, 2006). ...
... Several researchers have highlighted the benefits of using evolutionary design (Frazer, 2002;von Buelow, 2007;Janssen, 2006;Narahara et al., 2006). In addition, architectural design has benefited from the application of generative algorithm by adopting five different techniques: genetic algorithm, cellular automata, L-systems, swarm intelligence and shape grammars (Janssen, 2006). Indicative examples are presented in Table 3. ...
... The evolutionary design method uses evolutionary software systems (genetic algorithm) in order to enhance designers' ability during the design process. Evolutionary design is broadly recognised by the parametric evolutionary design and generative evolutionary design (Janssen, 2006). ...
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Purpose Problems relating ostensibly to failures in computational support for the conceptual design stage are well-documented in extant literature. These failures are multifarious and significant, with several deficiencies being acknowledged in the Architecture, Engineering, and Construction (AEC) industry. Whilst acknowledging this, extant literature has highlighted the importance of computational design in the AEC industry; and failures in this area include the need to strengthen the congruent links and support mechanisms in order to exploit the opportunities presented by new computational design methods. Given this, it is postulated that the application of generative design could enhance the design experience by assisting designers with the iterative generation of alternatives and parameterisation (change management) processes. Moreover, as Building Information Modelling (BIM) applications are increasingly providing comprehensive support for modelling and management, then additional synergies could be examined for further exploitation. Design/methodology/approach This paper focuses on the potential for developing an interactive BIM environment that purposefully adopts generative design as a method of computational design for the early design stages. This research facilitates the automation of the conceptual architectural design process, using BIM as the central conduit for enhancing the integration of the whole building design process (including design interfaces). This approach is designed to improve designers’ cognition and collaboration during the conceptual architectural design process. Findings This paper evaluates the existing methods and decision support mechanisms, and introduces the potential of combining different concepts into a single environment (generative design/BIM). Originality/value This research is novel in that it critically appraises virtual generative workspaces using BIM as the central conduit. The outcome and intervention of this research forms a theoretical basis for the development of a ‘proof of concept’ prototype, which actively engages generative design into a single dynamic BIM environment to support the early conceptual design process.
... The focus is more on physical form representation and computational support for the narrow design process (Chase, 2005;Oxman, 2006;Knight and Stiny, 2001). The recent development of the performance-based generative design method allows a more systematic search in the design space with scientific performance simulations and optimizations algorithms (Oxman, 2009;Janssen, 2006;Krish, 2011). However, most studies in the generative design school treat the general design process the same as the narrow design process which has a clearly defined design problem and rational process. ...
... (Janssen, 2006). ...
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Current planning and design decision support systems show limitations in the integration of design, science, and computation. Planning support systems with manual design and post-design evaluations impose major challenges in exploring huge design spaces. Generative design systems largely neglect the wicked nature of design problems and lack appropriate representation methods and simulation tools at the urban scale. To tackle those challenges, this research developed a Smart Design framework featuring urban design decision-making reinforced by artificial intelligence-aided design (AIAD). The Smart Design framework treats urban design as an emergent pattern formation processes with contextualized and dynamic objectives. The framework integrates design thinking, advanced artificial intelligence search techniques (e.g. genetic algorithms), urban scale performance simulations, and participation to better inform decision-making. Through four major stages, the framework combines the ideas of Science for Design and Design in Science. The significance and potential of the Smart Design framework are demonstrated in an urban design study of Gangnam superblocks in Seoul, South Korea. The study explores sustainable urban forms in the high-density, super-complex, and hyper-consumptive environment of Gangnam, which can also be found in many other Asian contexts. The case study illustrates how the framework identifies design solutions for sustainable city development in the process of participatory decision-making through the co-evolution of design problems and solutions.
... It is advocated that this approach could enhance the system's capabilities by allowing the generation of complex forms with various details and layout that would not be possible without using such a system. Several researchers have highlighted the benefits of using evolutionary design (Frazer 2002;Bentley 1999;Janssen 2006;Mirtschin 2011;Narahara et al. 2006). ...
... The evolutionary design method uses evolutionary software systems (such as genetic algorithm, cellular automata, L-systems, swarm intelligence and shape grammars) in order to enhance designers' ability during the design process. Evolutionary design is broadly recognised by the parametric evolutionary design and generative evolutionary design (Janssen 2006). ...
Article
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: New technological advancements in Architecture-Engineering-Construction (AEC) design has brought the 'level of automation' as a pivotal factor in the success of projects. One of the key debates in 'effective automation' is its congruence throughout the AEC projects. This is currently hampered due to the failures in computational support at the early conceptual design stages. Yet, these failures are significant, and have direct impact upon the success of the AEC design process. Extant literature has identified a significant knowledge gap concerning the key impact links and support mechanisms needed to overtly exploit computational design methods, especially Building Information Modelling (BIM), throughout the conceptual design stage. This study posited that integration of generative design algorithms to the existing BIM platforms could bridge this gap by generating design solutions and transforming them into next stages of detailed design. This paper reports on the conducted survey to investigate perceptions of 153 professionals and students and articulate their approach to different angles of such a technology. Most of the respondents highlighted several deficiencies in the existing tools, whilst they asserted that such a purposeful BIM interface can offer comprehensive support for automation of the entire of AEC design and implementation phases, and particularly enhance the decision making process at the early design phases. Building upon two main constructs of the conducted survey, namely information modelling and form generation, this study further developed conceptual framework for 'virtual generative design workspace' using BIM as the central conduit. The details of this framework are presented in this paper. The developed framework will be used to develop a 'proof of concept' prototype, to actively engage generative design methods into a single dynamic BIM environment. This study contributes by forming a stepping stone for digital integrating of all stages of AEC projects and implementing BIM Level 3 (Cloud), as targeted by many countries. COPYRIGHT: © 2014 The authors. This is an open access article distributed under the terms of the Creative Commons Attribution 3.0 unported (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
... Although Avital and Te'Eni mainly focus on the generative fit of a program, referring to Frazer [3] and Janssen [4], they write: in the case of artificial intelligence and other types of smart agents, information technology can be also modelled as an antecedent or source of a creative output [1]. ...
... Incorporating certain generative design (GD) algorithms, a program can inspire or challenge a designer by creating unique design alternatives [4]. ...
Conference Paper
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This work presents a concept of interactive machine learning in a human design process. An urban design problem is viewed as a multiple-criteria optimization problem. The outlined feature of an urban design problem is the dependence of a design goal on a context of the problem. We model the design goal as a randomized fitness measure that depends on the context. In terms of multiple-criteria decision analysis (MCDA), the defined measure corresponds to a subjective expected utility of a user. In the first stage of the proposed approach we let the algorithm explore a design space using clustering techniques. The second stage is an interactive design loop; the user makes a proposal, then the program optimizes it, gets the user's feedback and returns back the control over the application interface.
... This process is going to be developed by using Genetic algorithm by using specific routines to transform a set of genes (Genotype) to some specifications which have been affected by the input Genotype (Phenotype). The generative steps will start by defining the Grid positioning with considering the data files that includes the correct number of spaces, site boundary, and the number of floors, and other variables which can affect the spaces characteristics (Janssen, 2006). ...
... There also is a distinction between inner grid-faces that possess adjacent grid-faces on either side and outer grid-faces that are characterised by having only one neighbouring grid-face. Based on Janssen (2006) proposed approach, there are seven sequential generative phases a generative process consists of: positioning of the grid, the grid-faces translation, the outer grid-faces inclination, the staircase insertion, spaces creation, insertion of the doors and windows. A set of parameters is required by most of the phases to be encoded within the genotype, each always being encoded as real values in the range 0.0 to 1.0. ...
Article
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Purpose: The integration and automation of the whole design and implementation process has become a pivotal factor in construction projects. Problems of process integration, particularly at the conceptual design stage often manifest through a number of significant areas, from: design representation, cognition and translation, through to process fragmentation and loss of design integrity. Whilst Building Information Modelling (BIM) applications can be used to support design automation, particularly through the modelling, amendment, and management stages, they do not explicitly provide whole design integration. This is a significant challenge. However, advances in generative design now offers significant potential for enhancing the design experience to mitigate this challenge. Design/Methodology/Approach: The approach outlined in this paper specifically addresses BIM deficiencies at the conceptual design stage. Where, the core drivers and indicators of BIM and generative design are identified and mapped into a G-BIM framework, and subsequently embedded into a Generative BIM (G-BIM) prototype. This actively engages generative design methods into a single dynamic BIM environment to support the early conceptual design process. The developed prototype followed the CIFE “horseshoe” methodology of aligning theoretical research with scientific methods to procure Architecture, Construction and Engineering (AEC) – based solutions. This G-BIM prototype was also tested and validated through a focus group workshop engaging five AEC domain experts. Findings: The G-BIM prototype presents a valuable set of rubrics to support the conceptual design stage using generative design. It benefits from the advanced features of BIM tools in relation to illustration and collaboration (coupled with BIM’s parametric change management features). Research Limitations/Implications: This prototype has been evaluated through multiple projects and scenarios. However, additional test data is needed to further improve system veracity using conventional and non-standard real-life design settings (and contexts). This will be reported in later works. Originality/Value: Originality and value rests with addressing the shortcomings of previous research on automation during the design process. It also addresses novel computational issues relating to the implementation of generative design systems; where for example, instead of engaging static and formal description of the domain concepts, G-BIM actively enhances the applicability of BIM during the early design stages to generate optimised (and more purposeful) design solutions.
... Instead, prescriptive measurements for building codes based on human needs and manufacturing standards have dictated much of the human related aspects. It is with these standards that much of the computational design field works around, developing genetic algorithms [9] or form finding tools [8] that explore new shapes and aesthetics brought on by a variety of influences only recently possible through the advances in technology. In the physical space, industrial robotics has shown to be an important aspect in pushing the physical limits of building design and construction with conferences such as the biennial RobArch appearing in 2012 [3]. ...
Chapter
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From the invention of concrete to the yield point of steel, it is common knowledge that building design has been dictated in great part by the knowledge and capabilities of the available materials. Culture has also found a place in design, having strong influence of colors, space, and shape. Perhaps less discussed is the influence on building design from the cultural and scientific understanding of the human. As the methods of human simulation develop, so too does the understanding of the human. In this regard, computation is bringing yet another change in the influence of the human form on design. This paper provides a perspective of key historical points on the human form role in architecture and discusses the new role the human form plays with, such as the advancements of computation and simulation in regards to the usage of computational techniques for form finding. This discussion brings about the turning point for when design is inspired by humans to when it is based on humans.
... Many (e.g. Bentley and Kumar, 1999;Dawkins, 1988;Hornby, 2003;Janssen, 2006;McCormack, 1993;Ulieru and Doursat, 2011) have used generative processes to overcome the limitations and rigidity of top-down paradigms. COULD is different in encapsulating the generative rules within the adaptive cells. ...
Article
An algorithm has been developed to generate, without external intervention, a road and land-use plan for a regular or irregular site. It starts from an ‘embryo’ and grows a plan rather than trying to modify an initial solution. The basic modules are universal building blocks which change and adapt in a guided search with random selection of branching points followed by operations to add links or make connections. Deletion operators guide development by removing branches which do not improve the outcome. A hypothetical application, maximizing combined everyone-to-everyone connectivity and dwelling density, has evolved a highly interconnected street plan. However, no step is specific to the example; the operators will grow a road and land-use network under various specifications and constraints guided by an objective function. Making the process applicable to an actual development might require more constraints and certainly an enlarged objective function. Cost and other goals can be included so long as each goal is functionally related in some way to every change in the plan made by the search procedure.
... Development ('devo') generates complexity and evolution ('evo') optimizes the development. This shares similarities with previous works done by Bentley and Kumar (1999), Dawkins (1988), Frazer (1995, Hornby (2003), Janssen (2006), McCormack (1993, Ulieru and Doursat (2011). A feature of the proposed framework is the inclusion of agent-based modelling (ABM) and the emergent behaviour (Batty, 2007(Batty, , 2009, which are cornerstones of the 'devo' part. ...
Thesis
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This research presents an algorithm (COULD – COmputational Urban Layout Design) for generating residential development plans. Inspired by developmental biology, COULD can grow a plan from scratch or improve existing ones. The universal modules are the basic building blocks, which play similar roles to biological cells. They are ‘genetically identical’ with full developmental potential but will change physical form according to their local context. The ability to adapt is maintained throughout the developmental process during which modules sort out their dependencies through interactions. The use of generative genotype and its separation from phenotype give COULD characteristics of an emergent system.
... Ένα παραμετρικό μοντέλο δημιουργείται από έναν σχεδιαστή ο οποίος δηλώνει σαφώς πώς τα αποτελέσματα προκύπτουν από μια σειρά παραμέτρων» (Davis, 2013: 31). Κατά συνέπεια, η χρήση παραμετρικής λογικής είναι ένα καλός τρόπος ο σχεδιαστής να περιγράψει τη γεωμετρία του μοντέλου με ευελιξία, να δημιουργήσει εξαρτήσεις μεταξύ των σχέσεων των στοιχείων του, χωρίς αυτό να σημαίνει απαραίτητα ένα στυλ -όπως στο παραμετρικό μοντέλο μιας ορθοκανονικής μορφής κτηρίου (Janssen, 2006). Το παραμετρικό μοντέλο ορίζεται από κανόνες και περιορισμούς: αν αλλάξουμε έναν κανόνα ή περιορισμό, ή αν τροποποιήσουμε ένα στοιχείο του μοντέλου, αυτό θα επηρεάσει ολόκληρο το μοντέλο. ...
Conference Paper
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Ο αλγοριθμικός σχεδιασμός, ως μέθοδος εύρεσης μορφής και διαχείρισης πολύπλοκης γεωμετρίας στον αρχιτεκτονικό σχεδιασμό, αναδεικνύει την μετατόπιση της αρχιτεκτονικής σκέψης από την ψηφιακή αναπαράσταση της μορφής, την κατ’ εξοχήν πράξη του σχεδιασμού, στη συστηματική αναπαράσταση της σε παραμετρικό μοντέλο υπό τη μορφή κώδικα. Το μοντέλο αυτό ενσωματώνει την εσωτερική λογική οργάνωσης της μορφής, τις τοπολογικές σχέσεις και αλληλεξαρτήσεις των επιμέρους στοιχείων της, έτσι ώστε μια αλλαγή στις παραμέτρους που την περιγράφουν, να προκαλεί συντονισμένη ενημέρωση συνολικά. Δίνεται έτσι η δυνατότητα παραγωγής μιας σειράς παραλλαγών, οι οποίες εκφράζουν τις αρχικές σχεδιαστικές προθέσεις. Η κατανόηση αυτή του αλγοριθμικού σχεδιασμού ως ένα πρόβλημα καθορισμού του κώδικα που οργανώνει το παραμετρικό μοντέλο από τη φάση της αρχικής ιδέας μέχρι την τελική κατασκευή, αποτελεί το βασικό αντικείμενο του μεταπτυχιακού μαθήματος «Προηγμένα Ψηφιακά Εργαλεία στο Σχεδιασμό και την Κατασκευή» που γίνεται στη Σχολή Αρχιτεκτόνων Μηχανικών του Πολυτεχνείου Κρήτης από το 2015. Αναλύοντας τη θεωρία, εμπειρία και παραγωγή του συγκεκριμένου μαθήματος, η εισήγησή παρουσιάζει τις ιδιαιτερότητες και μετατοπίσεις του αλγοριθμικού σχεδιασμού με παραμετρικό μοντέλο σε σχέση με άλλες μεθόδους ψηφιακού σχεδιασμού. Η μέθοδος αυτή δίνει δυνατότητες αλληλεπίδρασης και ελέγχου του μοντέλου, παραγωγής διακριτών παραλλαγών σε ένα μεγάλο εύρος (αν και πεπερασμένο) δυνητικών αποτελεσμάτων. Αυτό είναι τελικά ένα αναδυόμενο χαρακτηριστικό φαινόμενο του ψηφιακού σχεδιασμού.
... Lawson's desire can potentially be realized if parametric modeling is integrated with such computational techniques as machine learning or data mining. The existing work involving machine learning techniques for the design process includes evolutionary design [23], [24] and agent based modeling [25]. ...
Conference Paper
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The ubiquitous computing era has pushed the Architecture, Engineering, and Construction (AEC) industry towards new frontiers of digitally enabled practice. Are these the frontiers originally identified by the pioneers in the field? Architectural design has progressively shifted from two-dimensional paper based pencil sketched models to digital models drawn in various Computer-Aided Design (CAD) tools. The recent adoption of parametric modeling tools from the aerospace industry has been driven by the need for tools that can assist in rapid flexible modeling. The adaptation of parametric modeling has reformed both pedagogy and practice of architectural design. The question remains if parametric design has answered all the requirements specified by Steven Anson Coons in his 1963 proposal for a Computer-Aided Design (CAD) system. Given the growth of computational power and ubiquitous computing, how has CAD met the visions of its pioneers with respect to the flexibility and ease of communication with the computer and support of simultaneous design conversations with many designers working on the same project? This paper will revisit ideas conceived by the early inventors of CAD, explore the opportunities for advancing parametric modeling with the existing ubiquitous computing infrastructure, and introduces the notion of software openness to support creativity and multidisciplinary design integration.
... Many (e.g. Bentley and Kumar, 1999;Dawkins, 1988;Hornby, 2003;Janssen, 2006;McCormack, 1993;Ulieru and Doursat, 2011) Sullivan and Haklay, 2000). The adaptive cells can be viewed as agents carrying the decision rules contained in the genotype. ...
Article
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The objective is to automate the design of residential layouts as an aid for planners dealing with complex situations. The algorithm COmputational Urban Layout Design, applied to sites with various shapes, is guided by the goal of many mutually accessible residences and can be set to generate orthogonal or irregular road layouts. Using biological principles of genomic equivalence, conditional differentiation and induction, it grows from an embryonic ‘adaptive cell’ into a plan. Cells are ‘genetically identical’ with full development potential and can simultaneously lay roads and residential lots, using the gene set to change cell expression and adapt to local contexts. Cells can be seen as self-propagating agents that sort out their dependencies through local interactions. When COmputational Urban Layout Design is set to grow a non-orthogonal layout, the plan has winding roads and irregular residential lots. Such a plan achieves the objective of relatively high residential density and accessibility, leading to walkable and coherent communities.
... The tools of urban form generation shall produce design options that are architecturally, geographically, historically and technically plausible. The Optimization step requires a plausible initial state or the design prototype from the Generation step to start with [77]. To generate a plausible initial state or design prototype, an understanding of the mechanism of how the city was formed and grew is a prerequisite. ...
Article
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This paper first defines the concept of energy-driven urban design. It aims to reveal synergies and trade-offs that may arise while designing urban areas for better energy performance. To facilitate urban planners and designers tackle these problems at the early stage of their work, this paper proposes the idea of simulation-based urban form generation and optimization modeling. It connects parametric models of urban form generation to an optimization engine coupled with a widely available program of energy systems.
... This act as collaboration in design, and specify the design concept as collaborative cognitive process. This participate in generative and evolutionary techniques in architecture [10]. There are digital spaces that participate in architectural design in such digital world that involve people to interact through its space [20], [2]. ...
Conference Paper
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- The paper reports on our experience to in adapting emotional experiences,of the software engineers in evolutionary design of software systems. The workshere reported present development,progress report in relation to the state-of art that need to create the multidisciplinary technologies, needed to establish best harmony engagement between human user the software application, based on humancognitive analysis. We approach the user best engagement from facial and voice analysis. And through it, we can measure (collectivized and quantified), and observe the user behavior, and accordingly enhance the engagement,by generative interactive scenario. The approach has been experimented using famous literature person (Keni Miyazawa). Keywords.Intelligent software, cognitive modeling, Human computer Interaction, software development, facial analysis, sound analysis. ,,,,,,,,,,,,,,
... The separation between those interpretations infers a data set description difference, solved by correctly addressing the interoperability. There is a need in integrating BIM standards (IFC) with generative evolutionary systems as exposed Janssen (2006) in the schema design method, with the seeding concept as a solution. The proposed method relies on rules to generate designs (evolutionary system), in addition to defining characters. ...
Article
Digital building modelling faces issues related to inconsistent integration/interoperability, in particular for a BIM GIS convergence. A consistent data conversion approach should consider semantic and spatial geometric modelling, therefore potentially leading to a loss of spatial geometric information. This paper simplifies semantic solving, and concentrates on the accuracy of the spatial geometric representation to avoid conflicting 3D spatial mismatches. To solve the geometry, original spatial specifications in 3D described in the IFC data schema are considered for all objects present in the model. The method is explored with a main BIM-IFC model, and additionally tested with two other models.
... Another approach is to create building massings composed of various small mass units. By arranging and rearranging these mass units, the algorithm can generate different spatial configurations [26][27][28][29]. This approach is also widely used in conjunction with thermal-zone layout optimization to achieve better energy efficiency [30,31]. ...
Article
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For sustainable building design, performance-based optimization incorporating parametric modelling and evolutionary optimization can allow architects to leverage building massing design to improve energy performance. However, two key challenges make such applications of performance-based optimization difficult in practice. First, due to the parametric modelling approaches, the topological variability in the building massing variants is often very limited. This, in turn, limits the scope for the optimization process to discover high-performing solutions. Second, for architects, the process of creating parametric models capable of generating the necessary topological variability is complex and time-consuming, thereby significantly disrupting the design processes. To address these two challenges, this paper presents a parametric massing algorithm based on the subtractive form generation principle. The algorithm can generate diverse building massings with significant topological variability by removing different parts from a predefined volume (food4rhino.com/node/2974). Additionally, the algorithm can be applied to different building massing design scenarios without additional parametric modelling being required. Hence, using the algorithm can help architects achieve an explorative performance-based optimization for building massing design while streamlining the overall design process. Two case studies of daylighting performance optimizations are presented, which demonstrate that the algorithm can enhance the exploration of the potential in building massing design for energy performance improvements.
... Janssen identifies some of its key attributes. " In order to evolve challenging designs, the variability of the designs must be carefully controlled so as to ensure that the designs are complex, intelligible, unpredictable and desirable" [40]. The genetic model is also an embodiment of knowledge of the design problem and solutions to it. ...
Article
A generative CAD based design exploration method is proposed. It is suitable for complex multi-criteria design problems where important performance criteria are uncomputable. The method is based on building a genotype of the design within a history based parametric CAD system and then, varying its parameters randomly within pre-defined limits to generate a set of distinctive designs. The generated designs are then filtered through various constraint envelopes representing geometric viability, manufacturability, cost and other performance related constraints, thus reducing the vast design space into a smaller viable design space represented by a set of distinctive designs. These designs may then be further developed by the designer. The proposed generative design method makes minimal imposition on the designer’s work process and maintains both flexibility and fluidity that is required for creative design exploration. Its ability to work seamlessly with current CAD based design practices from early conceptual to detailed design is demonstrated. The design philosophy behind this generative method and the key steps involved in its implementation are presented with examples.
... Authors compared different frames designed in generative designing to that of conventionally designed frames. The variability of the designs must be carefully controlled to advance various stimulating designs, such that the design generated will be comprehensible, unpredictable and most importantly desirable [6]. ...
Research
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This research accentuates to explore designing the drone frame using Generative design tools. A quadcopter is designed using Autodesk generative design embedded in Fusion 360. The simulation results such as static stress-strain, modal frequency and displacement results of additive manufactured quadcopter are compared with a DJI flame wheel F450 drone frame. The generative designed frame has minimum displacement compared to traditional designed drone frame. It is observed that generative designing technique along with additive manufactured frames yields better frames with improved resistance to fracture and minimum displacement compared to traditional designed DJI flame wheel F450 drone frame.
... Contrary to the common problem-solving practice in computer science in which a computerassisted-design target is specified by predefined parameters, 'generative evolutionary design' is focused on the generation or discovery of multiple new design alternatives that evolve from one another into unique and unexpected solutions. The generative design algorithm may be tuned for either triggering possible design alternatives that are potentially inspiring to designers or alternatives whose main purpose is to challenge an a priori given design (Janssen, 2006). Zandee (2004) proposed generative inquiry as a transformative process that offers an alternative to the rationally-structured theory development that is commonly used in social studies. ...
Article
Information systems (IS) research has been long concerned with improving task-related performance. The concept of fit is often used to explain how system design can improve performance and overall value. So far, the literature has focused mainly on performance evaluation criteria that are based on measures of task efficiency, accuracy, or productivity. However, nowadays, productivity gain is no longer the single evaluation criterion. In many instances, computer systems are expected to enhance our creativity, reveal opportunities and open new vistas of uncharted frontiers. To address this void, we introduce the concept of generativity in the context of IS design and develop two corresponding design considerations –‘generative capacity’ that refers to one's ability to produce something ingenious or at least new in a particular context, and ‘generative fit’ that refers to the extent to which an IT artefact is conducive to evoking and enhancing that generative capacity. We offer an extended view of the concept of fit and realign the prevailing approaches to human–computer interaction design with current leading-edge applications and users' expectations. Our findings guide systems designers who aim to enhance creative work, unstructured syntheses, serendipitous discoveries, and any other form of computer-aided tasks that involve unexplored outcomes or aim to enhance our ability to go boldly where no one has gone before. In this paper, we explore the underpinnings of ‘generative capacity’ and argue that it should be included in the evaluation of task-related performance. Then, we briefly explore the role of fit in IS research, position ‘generative fit’ in that context, explain its role and impact on performance, and provide key design considerations that enhance generative fit. Finally, we demonstrate our thesis with an illustrative vignette of good generative fit, and conclude with ideas for further research.
... AD is described by Janssen (2006) as an assembly of processes, that can be associative, dataflow, and procedural. The author further illustrates AD as a method to achieve specified objectives that may, or may not define a design movement (Janssen and Stouffs, 2015). ...
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... P. Bentley and J. Wakefield describe a generative evolutionary design system as capable of evolving a wide range of solid structures from scratch. This paper proposes a generative evolutionary design approach for truss topology synthesis, before truss shape optimization, based on work by P. Janssen in [24]. P. Janssen bases his generative evolutionary design method on earlier work by J. Frazer and J. Connor in [25], and J. Frazer in [26]. ...
... Each individual has genotype and phenotype representations. The genotype representation encodes information that can be used to create a model of the design, while the phenotype representation is the actual design model [20]. ...
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... As computation support, urban design computation is expected to address this plural dimension and facilitate public participation. However, although the visualization result can always be informative for the public, current urban design computation studies usually focused on the narrow design with well-defined design objectives (Quan et al., 2019) and centered the computation support on the professional designer exclusively, regardless of well-trained or novice, following the architectural design computation models and studies (Oxman, 2006;Janssen, 2006). This approach reflects the elitist idea of design for the public that professional designers take the control of design generation with the assumption that they can represent the public, thereby completely ignoring public participation. ...
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This paper describes an application of Adaptive Range Multiobjective Genetic Algorithms (ARMOGAs) to aerodynamic wing optimization. The objectives are to minimize transonic and supersonic drag coefficients, as well as the bending and twisting moments of the wings for the supersonic airplane. A total of 72 design variables are categorized to describe the wing's planform, thickness distribution, and warp shape. ARMOGAs are an extension of MOGAs with the range adaptation. Four-objective optimization was successfully performed. Pareto solutions are compared with Pareto optimal wings obtained by the previous three-objective optimization and a wing designed by National Aerospace Laboratory (NAL).
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We present a new inverse, interactive approach to acoustic design that applies optimization techniques to an acoustic simulation system. The work is motivated by the challenges inherent in achieving desirable acoustic behavior in 3D environments, and it builds on previous work in computer graphics dealing with the analogous problem in lighting design. The user interactively indicates a range of acceptable material and geometric modifications for an auditorium or similar space, and specifies acoustic goals by choosing target values for a set of acoustic measures. Given this set of goals and constraints, the system performs an optimization of surface material and geometric parameters using a combination of simulated annealing and steepest descent techniques. Visualization tools extract and present the simulated sound field data for points sampled in space and time. The user then manipulates the visualizations to create an intuitive expression of acoustic design goals. We demonstrate an i...
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Introduction This chapter describes our work in evolution of buildable designs using miniature plastic bricks as modular components. Lego 1 bricks are well known for their flexibility when it comes to creating low cost, handy designs of vehicles and structures. Their simple modular concept make toy bricks a good ground for doing evolution of computer simulated structures which can be built and deployed. Instead of incorporating an expert system of engineering knowledge into the program, which would result in more familiar structures, we combined an evolutionary algorithm with a model of the physical reality and a purely utilitarian fitness function, providing measures of feasibility and functionality. Our algorithms integrate a model of the physical properties of Lego structures with an evolutionary process that freely combines bricks of different shape and size into structures that are evaluated by how well they perform a desired function. The evolutionary p
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Chapter
This Workshop marks the thirtieth anniversary of the event which is normally regarded as the birth of modern design methodology and the design methods movement - the Conference on Design Methods held in London in 1962. The movement almost died in the 1970s, but seems now to have hung on to life and to have re-emerged and grown with some vigour in the last decade. This paper reviews this relatively short history of design methodology, maps out some of the major themes that have sustained it, and tries to establish some agreed understanding for the concepts of scientific design, design science and the science of design.
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As the field of computer-aided design has developed over the last thirty years or so, three clear paradigms have successively emerged. First, designing was conceived of as a problem-solving activity. Then, as the limitations of this view became increasingly apparent, designing was seen more as a Knowledge-based activity. (This is the perspective of most of the papers in this journal issue.) Now, there is a growing consensus that designing must be treated as a fundamentally social activity—a matter of multiple, autonomous but interconnected intelligences in complex interaction. Here I shall characterize and compare the three paradigms, discuss the implications of the newest of these, and go on to suggest some directions for the future applications of artificial intelligence techniques in design.
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Book
I Introduction.- Evolutionary Algorithms - An Overview.- Robust Encodings in Genetic Algorithms.- II Architecture and Civil Engineering.- Genetic Engineering and Design Problems.- The Generation of Form Using an Evolutionary Approach.- Evolutionary Optimization of Composite Structures.- Flaw Detection and Configuration with Genetic Algorithms.- A Genetic Algorithm Approach for River Management.- Hazards in Genetic Design Methodologies.- III Computer Science and Engineering.- The Identification and Characterization of Workload Classes.- Lossless and Lossy Data Compression.- Database Design with Genetic Algorithms.- Designing Multiprocessor Scheduling Algorithms Using a Distributed Genetic Algorithm System.- Prototype Based Supervised Concept Learning Using Genetic Algorithms.- Prototyping Intelligent Vehicle Modules Using Evolutionary Algorithms.- Gate-Level Evolvable Hardware: Empirical Study and Application.- Physical Design of VLSI Circuits and the Application of Genetic Algorithms.- Statistical Generalization of Performance-Related Heuristics for Knowledge-Lean Applications.- IV Electrical, Control and Signal Processing.- Optimal Scheduling of Thermal Power Generation Using Evolutionary Algorithms.- Genetic Algorithms and Genetic Programming for Control.- Global Structure Evolution and Local Parameter Learning for Control System Model Reductions.- Adaptive Recursive Filtering Using Evolutionary Algorithms.- Numerical Techniques for Efficient Sonar Bearing and Range Searching in the Near Field Using Genetic Algorithms.- Signal Design for Radar Imaging in Radar Astronomy: Genetic Optimization.- Evolutionary Algorithms in Target Acquisition and Sensor Fusion.- V Mechanical and Industrial Engineering.- Strategies for the Integration of Evolutionary/Adaptive Search with the Engineering Design Process.- Identification of Mechanical Inclusions.- GeneAS: A Robust Optimal Design Technique for Mechanical Component Design.- Genetic Algorithms for Optimal Cutting.- Practical Issues and Recent Advances in Job- and Open-Shop Scheduling.- The Key Steps to Achieve Mass Customization.
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Since the end of the last century it has commonly been seen as decadent to simply apply aesthetics to the structure of a building to make it beautiful (with the exception of the deliberately ironic, although irony itself would have been thought decadent by the stern moralists of the modern movement ). Architects such as Louis Sullivan, Mies Van der Rohe, Le Corbusier and so on used the example of engineering to help to explain the relationship between form and function. Based on the simplistic assumption that engineers do not design form, but that it emerges from the correct solution to mechanical realities (cf. the Eiffel tower, Brunel's bridges and the "dom-ino" concrete frame) the modern movement declared such objects as pure and right . The functionalist tradition has suffered many blows in the last 50 years, partly because it was always an oversimplification, and partly because technology has now reached a point where the constraints of structure have almost vanished, with form becoming the precursor of function rather than it's determinant, ie. anything is possible (cf. Utzon's Sydney Opera House, The new Bilbao gallery etc.) The study of evolutionary algorithms allows us to get back to a more rigorous analysis of the basic determinants of form, where the global form of an object not only should not but actually cannot be predetermined on an aesthetic whim. Thus with genetic algorithms we have an opportunity to experiment with the true determinants of form in a way that the pioneers of the modern movement would have relished - an aesthetic of pure function whose outcome is totally embedded in the problem to be solved. Published (author's copy) Peer Reviewed
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This dissertation dwells in the interstitial spaces between the fields of architecture, environmental design and computation. It introduces a Generative Design System that draws on evolutionary concepts to incorporate adaptation paradigms into the architectural design process. The initial aim of the project focused on helping architects improving the environmental performance of buildings, but the final conclusions of the thesis transcend this realm to question the process of incorporating computational generative systems in the broader context of architectural design. The Generative System [GS] uses a Genetic Algorithm as the search and optimization engine. The evaluation of solutions in terms of environmental performance is done using DOE2.1E. The GS is first tested within a restricted domain, where the optimal solution is previously known, to allow for the evaluation of the system's performance in locating high quality solutions. Results are very satisfactory and provide confidence to extend the GS to complex building layouts. Comparative studies using other heuristic search procedures like Simulated Annealing are also performed. The GS is then applied to an existing building by Alvaro Siza, to study the system's behavior in a complex architectural domain, and to assess its capability for encoding language constraints, so that solutions generated may be within certain design intentions. An extension to multicriteria problems is presented, using a Pareto-based method.
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This paper describes a generative evolutionary design system that aims to fulfil two key requirements: customisability and scalability. Customisability is required in order to allow the design team to incorporate personalised and idiosyncratic rules and representations. Scalability is required in order to allow large complex designs to be generated and evolved without performance being adversely affected. In order to fulfil these requirements, a computational architecture has been developed that differs significantly from existing evolutionary systems. In order to verify the feasibility of the this architecture, the generative process capable of creating three-dimensional building models has been implemented and demonstrated.
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In "An Evolutionary Architecture", John Frazer presents an overview of his work for the past 30 years. Attempting to develop a theoretical basis for architecture using analogies with nature's processes of evolution and morphogenesis. Frazer's vision of the future of architecture is to construct organic buildings. Thermodynamically open systems which are more environmentally aware and sustainable physically, sociologically and economically. The range of topics which Frazer discusses is a good illustration of the breadth and depth of the evolutionary design problem. Environmental Modelling One of the first topics dealt with is the importance of environmental modelling within the design process. Frazer shows how environmental modelling is often misused or misinterpreted by architects with particular reference to solar modelling. From the discussion given it would seem that simplifications of the environmental models is the prime culprit resulting in misinterpretation and misuse. The simplifications are understandable given the amount of information needed for accurate modelling. By simplifying the model of the environmental conditions the architect is able to make informed judgments within reasonable amounts of time and effort. Unfortunately the simplications result in errors which compound and cause the resulting structures to fall short of their anticipated performance. Frazer obviously believes that the computer can be a great aid in the harnessing of environmental modelling data, providing that the same simplifying assumptions are not made and that better models and interfaces are possible. Physical Modelling Physical modelling has played an important role in Frazer's research. Leading to the construction of several novel machine readable interactive models, ranging from lego-like building blocks to beermat cellular automata and wall partitioning systems. Ultimately this line of research has led to the Universal Constructor and the Universal Interactor. The Universal Constructor The Universal Constructor features on the cover of the book. It consists of a base plug-board, called the "landscape", on top of which "smart" blocks, or cells, can be stacked vertically. The cells are individually identified and can communicate with neighbours above and below. Cells communicate with users through a bank of LEDs displaying the current state of the cell. The whole structure is machine readable and so can be interpreted by a computer. The computer can interpret the states of the cells as either colour or geometrical transformations allowing a wide range of possible interpretations. The user interacts with the computer display through direct manipulation of the cells. The computer can communicate and even direct the actions of the user through feedback with the cells to display various states. The direct manipulation of the cells encourages experimentation by the user and demonstrates basic concepts of the system. The Universal Interactor The Universal Interactor is a whole series of experimental projects investigating novel input and output devices. All of the devices speak a common binary language and so can communicate through a mediating central hub. The result is that input, from say a body-suit, can be used to drive the out of a sound system or vice versa. The Universal Interactor opens up many possibilities for expression when using a CAD system that may at first seem very strange.However, some of these feedback systems may prove superior in the hands of skilled technicians than more standard devices. Imagine how a musician might be able to devise structures by playing melodies which express the character. Of course the interpretation of input in this form poses a difficult problem which will take a great deal of research to achieve. The Universal Interactor has been used to provide environmental feedback to affect the development of evolving genetic codes. The feedback given by the Universal Interactor has been used to guide selection of individuals from a population. Adaptive Computing Frazer completes his introduction to the range of tools used in his research by giving a brief tour of adaptive computing techniques. Covering topics including cellular automata, genetic algorithms, classifier systems and artificial evolution. Cellular Automata As previously mentioned Frazer has done some work using cellular automata in both physical and simulated environments. Frazer discusses how surprisingly complex behaviour can result from the simple local rules executed by cellular automata. Cellular automata are also capable of computation, in fact able to perform any computation possible by a finite state machine. Note that this does not mean that cellular automata are capable of any general computation as this would require the construction of a Turing machine which is beyond the capabilities of a finite state machine. Genetic Algorithms Genetic algorithms were first presented by Holland and since have become a important tool for many researchers in various areas.Originally developed for problem-solving and optimization problems with clearly stated criteria and goals. Frazer fails to mention one of the most important differences between genetic algorithms and other adaptive problem-solving techniques, ie. neural networks. Genetic algorithms have the advantage that criteria can be clearly stated and controlled within the fitness function. The learning by example which neural networks rely upon does not afford this level of control over what is to be learned. Classifier Systems Holland went on to develop genetic algorithms into classifier systems. Classifier systems are more focussed upon the problem of learning appropriate responses to stimuli, than searching for solutions to problems. Classifier systems receive information from the environment and respond according to rules, or classifiers. Successful classifiers are rewarded, creating a reinforcement learning environment. Obviously, the mapping between classifier systems and the cybernetic view of organisms sensing, processing and responding to environmental stimuli is strong. It would seem that a central process similar to a classifier system would be appropriate at the core of an organic building. Learning appropriate responses to environmental conditions over time. Artificial Evolution Artificial evolution traces it's roots back to the Biomorph program which was described by Dawkins in his book "The Blind Watchmaker". Essentially, artificial evolution requires that a user supplements the standard fitness function in genetic algorithms to guide evolution. The user may provide selection pressures which are unquantifiable in a stated problem and thus provide a means for dealing ill-defined criteria. Frazer notes that solving problems with ill-defined criteria using artificial evolution seriously limits the scope of problems that can be tackled. The reliance upon user interaction in artificial evolution reduces the practical size of populations and the duration of evolutionary runs. Coding Schemes Frazer goes on to discuss the encoding of architectural designs and their subsequent evolution. Introducing two major systems, the Reptile system and the Universal State Space Modeller. Blueprint vs. Recipe Frazer points out the inadequacies of using standard "blueprint" design techniques in developing organic structures. Using a "recipe" to describe the process of constructing a building is presented as an alternative. Recipes for construction are discussed with reference to the analogous process description given by DNA to construct an organism. The Reptile System The Reptile System is an ingenious construction set capable of producing a wide range of structures using just two simple components. Frazer saw the advantages of this system for rule-based and evolutionary systems in the compactness of structure descriptions. Compactness was essential for the early computational work when computer memory and storage space was scarce. However, compact representations such as those described form very rugged fitness landscapes which are not well suited to evolutionary search techniques. Structures are created from an initial "seed" or minimal construction, for example a compact spherical structure. The seed is then manipulated using a series of processes or transformations, for example stretching, shearing or bending. The structure would grow according to the transformations applied to it. Obviously, the transformations could be a predetermined sequence of actions which would always yield the same final structure given the same initial seed. Alternatively, the series of transformations applied could be environmentally sensitive resulting in forms which were also sensitive to their location. The idea of taking a geometrical form as a seed and transforming it using a series of processes to create complex structures is similar in many ways to the early work of Latham creating large morphological charts. Latham went on to develop his ideas into the "Mutator" system which he used to create organic artworks. Generalising the Reptile System Frazer has proposed a generalised version of the Reptile System to tackle more realistic building problems. Generating the seed or minimal configuration from design requirements automatically. From this starting point (or set of starting points) solutions could be evolved using artificial evolution. Quantifiable and specific aspects of the design brief define the formal criteria which are used as a standard fitness function. Non-quantifiable criteria, including aesthetic judgments, are evaluated by the user. The proposed system would be able to learn successful strategies for satisfying both formal and user criteria. In doing so the system would become a personalised tool of the designer. A personal assistant which would be able to anticipate aesthetic judgements and other criteria by employing previously successful strategies. Ultimately, this is a similar concept to Negroponte's "Architecture Machine" which he proposed would be computer system so personalised so as to be almost unusable by other people. The Universal State Space Modeller The Universal State Space Modeller is the basis of Frazer's current work. It is a system which can be used to model any structure, hence the universal claim in it's title. The datastructure underlying the modeller is a state space of scaleless logical points, called motes. Motes are arranged in a close-packing sphere arrangement, which makes each one equidistant from it's twelve neighbours. Any point can be broken down into a self-similar tetrahedral structure of logical points. Giving the state space a fractal nature which allows modelling at many different levels at once. Each mote can be thought of as analogous to a cell in a biological organism. Every mote carries a copy of the architectural genetic code in the same way that each cell within a organism carries a copy of it's DNA. The genetic code of a mote is stored as a sequence of binary "morons" which are grouped together into spatial configurations which are interpreted as the state of the mote. The developmental process begins with a seed. The seed develops through cellular duplication according to the rules of the genetic code. In the beginning the seed develops mainly in response to the internal genetic code, but as the development progresses the environment plays a greater role. Cells communicate by passing messages to their immediate twelve neighbours. However, it can send messages directed at remote cells, without knowledge of it's spatial relationship. During the development cells take on specialised functions, including environmental sensors or producers of raw materials. The resulting system is process driven, without presupposing the existence of a construction set to use. The datastructure can be interpreted in many ways to derive various phenotypes. The resulting structure is a by-product of the cellular activity during development and in response to the environment. As such the resulting structures have much in common with living organisms which are also the emergent result or by-product of local cellular activity. Primordial Architectural Soups To conclude, Frazer presents some of the most recent work done, evolving fundamental structures using limited raw materials, an initial seed and massive feedback. Frazer proposes to go further and do away with the need for initial seed and start with a primordial soup of basic architectural concepts. The research is attempting to evolve the starting conditions and evolutionary processes without any preconditions. Is there enough time to evolve a complex system from the basic building blocks which Frazer proposes? The computational complexity of the task being embarked upon is not discussed. There is an implicit assumption that the "superb tactics" of natural selection are enough to cut through the complexity of the task. However, Kauffman has shown how self-organisation plays a major role in the early development of replicating systems which we may call alive. Natural selection requires a solid basis upon which it can act. Is the primordial soup which Frazer proposes of the correct constitution to support self-organisation? Kauffman suggests that one of the most important attributes of a primordial soup to be capable of self-organisation is the need for a complex network of catalysts and the controlling mechanisms to stop the reactions from going supracritical. Can such a network be provided of primitive architectural concepts? What does it mean to have a catalyst in this domain? Conclusion Frazer shows some interesting work both in the areas of evolutionary design and self-organising systems. It is obvious from his work that he sympathizes with the opinions put forward by Kauffman that the order found in living organisms comes from both external evolutionary pressure and internal self-organisation. His final remarks underly this by paraphrasing the words of Kauffman, that life is always to found on the edge of chaos. By the "edge of chaos" Kauffman is referring to the area within the ordered regime of a system close to the "phase transition" to chaotic behaviour. Unfortunately, Frazer does not demonstrate that the systems he has presented have the necessary qualities to derive useful order at the edge of chaos. He does not demonstrate, as Kauffman does repeatedly, that there exists a "phase transition" between ordered and chaotic regimes of his systems. He also does not make any studies of the relationship of useful forms generated by his work to phase transition regions of his systems should they exist. If we are to find an organic architecture, in more than name alone, it is surely to reside close to the phase transition of the construction system of which is it built. Only there, if we are to believe Kauffman, are we to find useful order together with environmentally sensitive and thermodynamically open systems which can approach the utility of living organisms.
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OF THE DISSERTATION GADO: A Genetic Algorithm for Continuous Design Optimization by Khaled Mohamed Rasheed Dissertation Director: Haym Hirsh Genetic algorithms (GAs) have been extensively used as a means for performing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains a general purpose GA is often inefficient and unable to reach the global optimum. In this thesis we describe a GA for continuous designspace optimization that uses new GA operators and strategies tailored to the structure and properties of engineering design domains. Empirical results in several realistic engineering design domains as well as benchmark design domains demonstrate that using our system can greatly decrease the cost of design space search, and can also improve the quality of the resulting designs. ii Acknowledgements I thank all members of the Rutgers HPCD (Hypercomputing and Design) project. In particular, I thank my advisors, Haym Hirs...
A conceptual seeding technique for architectural design
  • J H Frazer
  • J Connor
Frazer, J. H. and Connor, J. (1979) 'A conceptual seeding technique for architectural design', Proceedings of International Conference on the Application of Computers in Architectural Design and Urban Planning (PArC79), AMK, Berlin, pp. 425-434.