Adaptive-growth-type 3D representation for configuration 
design

Taura, Toshiharu; Nagasaka, Ichiro

doi:10.1017/s089006049913302x

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…More standard CAs are used by Bentley and Kumar (1999) for creating two-dimensional tessellating tile patterns and for creating patterns of cells in an isospatial grid (Bonabeau et al, 2000). Rather than working in a grid, the shape feature generating process (SFGP) grows designs by optimizing rules for the division of dots (metaphors for a cell) on the surface of a shape (Taura and Nagasaka, 1999;Taura et al, 1998). After development is complete, the final shape is formed by creating an outer surface by using the density of dots to determine the distance from the initial shape.…”

Section: Review Of Automated Design Representationsmentioning

confidence: 99%

Functional Scalability through Generative Representations: The Evolution of Table Designs

Hornby

2004

Environ Plann B Plann Des

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One of the main limitations for the functional scalability of automated design systems is the representation used for encoding designs. I argue that generative representations, those which are capable of reusing elements of the encoded design in the translation to the actual artifact, are better suited for automated design because reuse of building blocks captures some design dependencies and improves the ability to make large changes in design space. To support this argument I compare a generative and a nongenerative representation on a table-design problem and find that designs evolved with the generative representation have higher fitness and a more regular structure. Additionally the generative representation was found to capture better the height dependency between table legs and also produced a wider range of table designs.

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Section: Review Of Automated Design Representationsmentioning

confidence: 99%

Functional Scalability through Generative Representations: The Evolution of Table Designs

Hornby

2004

Environ Plann B Plann Des

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“…Cellular automata have been used as a model of development to generate simple shapes [21,22], biological processes (e.g., gastrulation and limb budding) [26], or specific 2D [6] and 3D [2] target patterns. Rules that fire cell functions, such as mitosis, apoptosis, or migration, have been implemented to design 3D geometrical shapes [78], tessellating tiles in a grid [3], and 3D morphologies [38]. Rules have also been combined with gene regulatory networks to control their activation in the evolution of 2D shapes [14], 2D patterns [81,19], and 3D multicellu-lar organisms [15,16,31].…”

Section: A Graph-grammar-based Developmental Model For Behavior-findingmentioning

confidence: 99%

Behavior-Finding: Morphogenetic Designs Shaped by Function

Lobo

Fernández

Vico

2012

Morphogenetic Engineering

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Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism's morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design "organic" structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding.

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“…Some GP practitioners regard all three to be the same. Others, such as Jakobi's work on evolving neural networks [33] or Taura's work on evolutionary configuration design [34], use different and distinct representations for each stage. It is still unclear whether the component growing process of the embryogeny should use the same representation as the phenotype -should the phenotype be represented by blocks or should the blocks be merged into a single, whole description of the solution?…”

Section: Genotype Representationmentioning

confidence: 99%

Exploring Component-based Representations - The Secret of Creativity by Evolution?

Bentley

2000

Evolutionary Design and Manufacture

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Abstract. This paper investigates one of the newest and most exciting methods in computer science to date: employing computers as creative problem solvers by using evolution to explore for new solutions. The paper introduces and discusses the new understanding that explorative evolution relies upon a representation based on components rather than a parameterisation of a known solution. Evolution explores how the components can be arranged, how many are needed, and the type or function of each. The extra freedom provided by this simple idea is remarkable. By using evolutionary computation for exploration instead of optimisation, this technique enables us to expand the capabilities of computers. The paper describes how the approach has already shown impressive results in the creation of novel designs and architecture, fraud detection, composition of music, and creation of art. A framework for explorative evolution is provided, with discussion of the significance and difficulties posed by each element. The paper ends with an example of creative problem solving for a simple application -showing how evolution can shape pieces of paper to make them fall slowly through the air, by spiraling down like sycamore seeds.

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Adaptive-growth-type 3D representation for configuration design

Cited by 13 publications

References 9 publications

Functional Scalability through Generative Representations: The Evolution of Table Designs

Functional Scalability through Generative Representations: The Evolution of Table Designs

Behavior-Finding: Morphogenetic Designs Shaped by Function

Exploring Component-based Representations - The Secret of Creativity by Evolution?

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