Graph design by graph grammar evolution

Luerssen, Martin H.; Powers, David

doi:10.1109/cec.2007.4424497

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…plants and animals [18], [21], [24], and generating programs, graphs and networks with different features and properties [15], [19], [29], [31]. As a result, different graph representation systems have been developed ranging from the traditional adjacency matrix [14] to formal language based systems, e.g.…”

Section: A Languages For Encoding Scenario Representationmentioning

confidence: 99%

An expressive GL-2 grammar for representing story-like scenarios

Bui

Bender

Abbass

2012

2012 IEEE Congress on Evolutionary Computation

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This paper extends our previous work on evolving stories towards a computational platform for automatic scenario generation. In particular, we address a shortcoming of our earlier work relating to scenario representation: the regular story plot grammar. The use of this grammar, which only captures causal relationships between plot elements from the point of view of a single story character, resulted in the generation of stories that are always associated with one main character only, limiting the scalability of the approach. In addition, the regular grammar employed is not suitable for representing practical scenarios since a practical scenario may not require a character at all. To overcome these problems, we propose a new approach to scenario representation. Firstly, we introduce a set of scenario building blocks based on narrative theory. As a result, a scenario can be represented as a network of these building blocks that captures various relationships in the scenario. The task of generating scenarios is then transformed to the task of generating networks of these building blocks. Secondly, we develop two network representation languages extending Boers' GL-2 graph representation systems to describe networks with edges of more than one type and edges between two groups of nodes. Thirdly, a set of two context-free grammars is introduced to generate sentences, i.e. scenarios in these languages. Finally, we verify our approach to strategic scenario generation by employing an interactive evolution framework, which shows the proposed scenario representation scheme can facilitate the generation of coherent and novel story-like scenarios.

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Section: A Languages For Encoding Scenario Representationmentioning

confidence: 99%

An expressive GL-2 grammar for representing story-like scenarios

Bui

Bender

Abbass

2012

2012 IEEE Congress on Evolutionary Computation

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“…Previously explored benefits of SGE are performance benefits from sharing subsolutions results between solutions [3] and extending the grammar to more complex data structures such as graphs [11]. A drawback of SGE is that crossreferencing between productions can be extensive, leading to rapid growth in solution size beyond what is necessary for problem solving.…”

Section: Ndmentioning

confidence: 99%

Evolvability and Redundancy in Shared Grammar Evolution

Luerssen

Powers

2007

2007 IEEE Congress on Evolutionary Computation

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Abstract-Shared grammar evolution (SGE) is a novel scheme for representing and evolving a population of variablelength programs as a shared set of grammatical productions. Productions that fail to contribute to selected solutions can be retained for several generations beyond their last use. The ensuing redundancy and its effects are assessed in this paper on two circuit design tasks associated with random number generation: finding a recurrent circuit with maximum period, and reproducing a De Bruijn counter from a set of seed/output pairs. In both instances, increasing redundancy leads to significantly higher success rates, outperforming comparable increases in population size. The results support previous studies that have shown that representational redundancy can be beneficial to evolutionary search. However, redundancy promotes an increase in further redundancy by encouraging the creation of large offspring, the evaluation of which is computationally costly. This observation should generalize to any unconstrained variablelength representation and therefore represents a notable drawback of redundancy in evolution.

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“…Possibilities expressing these actions as rules in evolutionary design have been explored by John Frazer using genetic algorithms since 1968 [7]. Evolutionary design is a generative method that has been used for many years to synthesize and evaluate designed objects during the design process [8,9]. Evolutionary design will be used to stimulate creativity in generating new, unexpected forms of building for evaluating the designer's aesthetic preferences.…”

Section: Introductionmentioning

confidence: 99%

Generative and Evolutionary Techniques for the Process of Creating Architectural Objects on the Base of a 3D Prototype Model

Grabska

2022

Buildings

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The use of a genetic algorithm in evolutionary design is one of the major generative approaches for synthesis and evaluation during the design process. The process stimulates creativity in generating new, unexpected artifacts and aiding in their evaluation. We analyze the subject of the evolutionary design of building form styling following the aesthetic preferences of the designer. Component types and connection patterns characterize the building form and the rules of its composition. The designer using a graphics editor creates a 3D prototype model of a building form representative of his/her stylistic preferences by selecting different types of components and patterns of their connections. In the proposed evolutionary design, how the designer prototype model is organized, processed, and manipulated in generating buildings is based on the special graph structures. The research question addressed in this paper is, “How can a designer-defined 3D prototype model along with non-numerical graph calculations, influence computational creativity?” The main aim is to contribute to a better understanding of the non-numerical graph calculations describing the design process where visual perception is the driving force of creativity. Utilizing the developed formal description of the design synthesis, methodological contributions to generative and evolutionary techniques for computational creativity are presented.

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Graph design by graph grammar evolution

Cited by 9 publications

References 32 publications

An expressive GL-2 grammar for representing story-like scenarios

An expressive GL-2 grammar for representing story-like scenarios

Evolvability and Redundancy in Shared Grammar Evolution

Generative and Evolutionary Techniques for the Process of Creating Architectural Objects on the Base of a 3D Prototype Model

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