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life ID: 2222255, generation 207

genetics & simulation

For HD:R1 genetic (genetics may be applied in architecture in a real and natural way, it is real genetic architecture for which information is necessary combined contribution from both architects and geneticists, see definitions) approach was chosen.
However it was impossibe to create a real genetic example as there are no geneticists in authors friends list. ...Joining genetics and digi, creating a simulation of genetic architecture?

Digital onscreen genetics may consist of:

• Morphogenetics
  Algorithmic architecture that is based on fractal systems, Lindenmayer systems and genetic algorithms for generating recursively defined geometrical objects
• Biomimetics
  Biological systems are being imitated using the benefits of computation processes.
• Bionic architecture
  Architecture that’s functioning, systems or processes are similar as in living beings.
• Organic Architecture
  Architecture that in unity and continuity merges elements that are responsible for various functions, as it may be seen in living organisms. Especially it merges bifurcation between supporting and supported elements into one continuous shape.
• Genetic architecture
  genetics may be applied in architecture in a metaphoric and artistic way. It is genetic architecture only by extension or similarity to genetic definitions and processes.

and it simulates the possibe genetic future:

	Classical past	Modern present	Genetic future
Chronology	Until 19th century	20th century	Starting with 20th century
Formal system	Verticalisation	Horizontalisation	Organic forming
Material system	Stone, brick and wood	Concrete, steel, glass and plastic	Software controlled DNA: plants, flesh and bone
Process and construction system	Manual processing of every part	Mass unified production by machinery for every similar part	Automatised production and natural growth of different parts

some of computational methods in reach

artificial life

• Cellular automata
• Lindenmayer system
• Neural networks

evolutionary algorithms

• Genetic algorithm
• Genetic programming
• Evolutionary programming
• Evolution strategy
• Swarm intelligence

pathfinding

• A* search algorithm
• B* search algorithm
• Beam search algorithm
• Bellman-Ford algorithm
• Best-First-Search (BFS) algorithm
• Bidirectional search algorithm
• Breadth-first search (BFS) algorithm
• D* search algorithm
• Depth-first search algorithm
• Depth-limited search algorithm
• Dijkstra’s algorithm
• Floyd–Warshall algorithm
• Iterative deepening depth-first search (IDDFS) algorithm
• Johnson’s algorithm
• Lexicographic breadth-first search (Lex-BFS) algorithm
• Uniform-cost search algorithm

physics

• Real world forces and those within life
• Runge–Kutta method
• Euler’s formula

sensors

• Consciousness, part of life

where?

say Vienna & calling.

What would Francis I say, if in the main atrium of a building supervised by him and completed in 1815, one would throw in a seed for organism

• which grows
• uses existing structures as food
• maintains the structural integrity and stability of existing structures
• is photosensitive, depends on light
• feels humans moving through it and avoids unnecessary confrontation (think of paths in woods)
• if humans treat it kindly, forms cells for a midday shuteye
• is sensitive to touches (think of snail tentacles)
• likes warmth
• follows laws of physics (gravity, inner tension, outer forces, collisions)

tools

Language used- Java
Libraries- Bullet Physics, OpenGL, Traer physics, vector and polygon math libraries, OBJ importers, sensor drivers, a.o.

the algorithm

1 set up simulation environment

Load all necessary code, set global constants, variables

2 get world space

Load existing model or read new using sensors

3 get world paremeters and place into environment

Get space dimensions, mins and maxes, place into environment

3 apply physics laws

Set up space physics - gravity, drag, collisions, forces between objects

4 initialise life

Set up life variables, constants, size, characteristics, selection, reproduction, termination rules

5 pass world to life knowing

Life reads world, sets up key points (“the seed points”)

6 pass sensors to life knowing

Life reads human paths, vibration, light, temperature etc.

7 simulate

Selection, Reproduction, Termination, Physics, Rules, Form, Structure

8 control

Pause, start, stop, reset

9 evaluation by randomness

Export, visualise

life growing

life ID: 230596 Generation: 200 Frame: 236

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