Combining Structural Analysis and Multi-Objective Criteria for Evolutionary Architectural Design

Byrne, Jonathan; Fenton, Michael; Hemberg, Erik; McDermott, James; O’Neill, Michael; Shotton, Elizabeth; Nally, Ciaran

doi:10.1007/978-3-642-20520-0_21

Cited by 25 publications

(19 citation statements)

References 15 publications

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“…GE has been used extensively for automatic design with promising results [3,1,7] motivating the exploration of its applicability for automatic CG [11,12,2] . In our implementation of GE, the phenotype (a timeline) is a one-dimensional string of interaction events (IEs).…”

Section: Timeline Generationmentioning

confidence: 99%

A Progressive Approach to Content Generation

Shaker

Togelius

et al. 2015

Applications of Evolutionary Computation

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Abstract. PCG approaches are commonly categorised as constructive, generateand-test or search-based. Each of these approaches has its distinctive advantages and drawbacks. In this paper, we propose an approach to Content Generation (CG)-in particular level generation -that combines the advantages of constructive and search-based approaches thus providing a fast, flexible and reliable way of generating diverse content of high quality. In our framework, CG is seen from a new perspective which differentiates between two main aspects of the gameplay experience, namely the order of the in-game interactions and the associated level design. The framework first generates timelines following the search-based paradigm. Timelines are game-independent and they reflect the rhythmic feel of the levels. A progressive, constructive-based approach is then implemented to evaluate timelines by mapping them into level designs. The framework is applied for the generation of puzzles for the Cut the Rope game and the results in terms of performance, expressivity and controllability are characterised and discussed.

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Section: Timeline Generationmentioning

confidence: 99%

A Progressive Approach to Content Generation

Shaker

Togelius

et al. 2015

Applications of Evolutionary Computation

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“…GE has been used extensively for automatic design [13], [14], [15], a domain where it has been shown to have a number of strengths over more traditional optimization methods; it maintains a simple way of describing the structure of the levels and it enables the design of aesthetically pleasing levels by exploring a wide space of possibilities. The use of GE for the automatic generation of game content was first explored by Shaker et.…”

Section: Grammatical Evolutionmentioning

confidence: 99%

Automatic generation and analysis of physics-based puzzle games

Shaker

Sarhan

Naameh

et al. 2013

2013 IEEE Conference on Computational Inteligence in Games (CIG)

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Abstract-In this paper we present a method for the automatic generation of content for the physics-based puzzle game Cut The Rope. An evolutionary game generator is implemented which evolves the design of levels based on a context-free grammar. We present various measures for analyzing the expressivity of the generator and visualizing the space of content covered. We further perform an experiment on evolving playable content of the game and present and analyze the results obtained.

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“…[11]. One line of research used GE to explore spaces of 3D designs such as bridges and pylons consisting of uniform variable-length beams [12,13].…”

Section: Generative 3d Designmentioning

confidence: 99%

Program Optimisation with Dependency Injection

McDermott

Carroll

2013

Lecture Notes in Computer Science

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Abstract. For many real-world problems, there exist non-deterministic heuristics which generate valid but possibly sub-optimal solutions. The program optimisation with dependency injection method, introduced here, allows such a heuristic to be placed under evolutionary control, allowing search for the optimum. Essentially, the heuristic is "fooled" into using a genome, supplied by a genetic algorithm, in place of the output of its random number generator. The method is demonstrated with generative heuristics in the domains of 3D design and communications network design. It is also used in novel approaches to genetic programming.

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Combining Structural Analysis and Multi-Objective Criteria for Evolutionary Architectural Design

Cited by 25 publications

References 15 publications

A Progressive Approach to Content Generation

A Progressive Approach to Content Generation

Automatic generation and analysis of physics-based puzzle games

Program Optimisation with Dependency Injection

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