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cellular automata in architectural design
























Cellular automata is the computational method which can simulate the process of growth by describing a complex system by simple individ uals following simple rules. This concept of simulating growth was introduced by John von Neumann and further developed by Ulam in the area of simulating multi-state machines. The concept gained greater popularity when Martin Gardner described John Conway’s “Game of Life" that generated two-dimensional patterns. Stephen Wolfram began researching the concept to represent physical phenomena and has recently reintroduced the discussion in “A New Kind of Science”. The connection to architecture is the ability of cellular automata to generate patterns, from organized patterns we might be able to suggest architectural forms. Cellular automata, viewed as a mathematical approach, differs from a traditional deterministic methods in that current results are the basis for the next set of results.


genetic algorithm in architectural design























Genetic Algorithms were invented by John Holland in the 1960s and since then they have been used as stochastic methods for solving optimization and search problems, operating on a population of possible solutions. According to Darwin’s Theory of Evolution, the repetitive application of the aforementioned procedures alters an initial species into various other species; however, only the stronger prevail. Genetic Algorithms perform the same operations on the population of possible targets with only those that fit the solution better surviving.


While other disciplines have adopted computational tools based on the principles of evolutionary biology, in architectural design evolutionary processes have not been broadly applied. Only recently has there been a noticeable shift in the way architects explore such techniques to address complex problems. Indeed, one of the main problems in architecture today is the quantity of information and the level of complexity involved in most building projects. Genetic Algorithms offer an effective solution to this problem by solving optimization and search problems, operating on a population of possible solutions. In architecture GAs operate in two ways: as optimization tools and as form-generation tools. In the first way GAs address well-defined building problems, such as structural, mechanical, and thermal and lighting performance. In the second way GAs are used under the scope of the concept of Emergence.

inaBakunowicz_ANN_3D SOM_surfaces_50x50

artificial neural networks in architectural design






















An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example.


An ANN is configured for a specific application, such as pat-tern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well.


agent-based modelling in architectural design




























An agent-based model (ABM) is one of a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming.


Agent-based models are a kind of microscale model [3] that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergence from the lower (micro) level of systems to a higher (macro) level. As such, a key notion is that simple behavioral rules generate complex behavior. This principle, known as K.I.S.S. ("Keep it simple, stupid") is extensively adopted in the modeling community. Another central tenet is that the whole is greater than the sum of the parts. Individual agents are typically characterized as boundedly rational, presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status,[4] using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction.

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AmiinaBakunowicz_DiffusionAlgorithm_MeltingTower (4)
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