• h

  • BG


  • morph

        • 20 generations x 20 organisms
        •  

          Every building object of evolution requires an algorithm, able to generate future configurations of this building. The most powerful algorithm, good enough to solve optimization problems and find unexpected solutions, is called Genetic Algorithm (GA). It consists of a population of a particular number of generations, containing a particular number of solutions. The first generation organisms are usually produced using arbitrary numbers and they are not working ones, the working solution is achieved during the execution of the Genetic Algorithm. The next generations are made by mixing the genes of chosen parents from the previous generation.



           

          The basic parts of a classical (GA) are:


           

          1. Function that generates the organisms of the first generation.


           

          2. Function that evaluates the solutions (fitness-function).


           

          3. Function that chooses the parents of the next generation.


           

          4. Crossover function. It mixes the genetic material of the two parents.


           

          5. Mutation function.
        •  

          The average score increases with every generation, showing that the different parts of the algorithm are relatively well-constructed.
          Because of visualization purposes, a set of twenty generations is chosen. The best organism, achieved after these twenty generations, is good enough for demonstration, so more generations are not necessary.
        • 15 puzzle  

          The chromosomes have tree structures. The first level of the structure corresponds to the vertical communication (black cells), the second represents the horizontal one (grey cells) and the third corresponds to the rooms (color cells). The genetic code is a list of functions that define the geometry of the organism:



           

          F - places a cell and translates the coordinate system one unit along X axis.


           

          Y+ and Y- - rotate the coordinate system 90 degrees around Z axis.


           

          Z - translates the coordinate system one unit along Z axis.


           

          ) and ( - moving to lower or higher level of the tree.
        • evolutionary building - genetic algorithm - best solution best solution
        • evolutionary building - genetic algorithm - best solution best solution

        • best solution - chromosome



          ((F (F F (Y+ F)) (Y+ Y+ F Y+ F (Y+ F)) (Y+ F Y- F Y+ F (F) ) (Y+ Y+ F F (Y+ F Y- F)) (F F F (F Y- F)) (F Y- F F (F Y+ F)) (Y+ Y+ F Y- F F (Y- F Y- F Y- F)) (Y- F F (Y- F Y+ F Y+ F Y+ F))) Z Y- (F (Y- F Y- F Y- F (F)) (F Y+ F Y+ F (F)) (F Y+ F Y- F (Y- F)) (F F F (Y- F)) (Y- F Y+ F Y- F (Y- F)) (Y+ Y+ F Y+ F F (Y- F Y- F)) (F Y+ F F (Y- F F)) (Y- F Y- F F (Y- F Y+ F Y+ F Y+ F))) Z (F (Y- F Y+ F F (Y+ F)) (F Y- F F (F)) (F Y+ F F (F)) (Y+ F F F (F Y- F)) (Y+ F Y- F F (F F)) (Y- F Y- F Y+ F (Y+ F Y- F)) (Y- F Y- F Y- F (Y+ F Y- F Y- F Y- F))) Z Y- (F (F Y+ F Y- F (Y+ F)) (Y+ F F Y- F (Y- F)) (Y+ F F F (F)) (Y+ Y+ F Y+ F F (Y- F)) (F F (F)) (F Y- F F (F)) (Y- F F (Y- F)) (Y+ Y+ F F F (Y- F)) (Y+ F Y+ F (Y+ F Y- F)) (Y+ Y+ F F Y+ F (Y- F Y+ F Y+ F Y+ F))) Z Y+ (F (Y+ F Y- F Y+ F (Y+ F)) (Y+ F Y+ F Y- F (Y- F)) (F Y+ F Y- F (Y- F)) (Y+ Y+ F Y+ F Y+ F (F)) (Y+ F Y+ F F (Y+ F Y+ F)) (F F (F Y- F)) (Y+ Y+ F Y+ F F (Y- F Y+ F Y+ F Y+ F))) Z (F (Y- F F F (Y+ F)) (Y+ Y+ F Y- F F (Y+ F)) (Y+ Y+ F Y+ F (Y- F)) (Y- F F Y- F (Y+ F)) (F Y- F Y+ F (Y+ F Y- F)) (Y+ Y+ F F Y+ F (Y- F Y- F)) (Y+ Y+ F F F (F F)) (Y+ F Y+ F F (F F)) (Y+ F Y- F Y+ F (Y- F Y+ F Y+ F Y- F))))

  • evolution


    and


    script
        • download script - (Autolisp)
        •  

          The algorithm not only generates the first configuration, but it is also able to give a new one when it is needed.
          The reasons for starting the application again are various: changed number of occupants, changed function of a cell, cyclic evolution
          (looking for better sites of some rooms during the different seasons), even a change in the brief
          (if the main computer realizes that the area of some rooms needs correction - room for two children - from one cell it becomes two etc.).
          Four new configurations, using the same algorithm, were generated to demonstrate some possible new solutions if the number of inhabitants changes.
        • evolutionary building - genetic algorithm - solution for 4 mothers and 30 children 4 mothers / 30 children
        • evolutionary building - genetic algorithm - solution for 3 mothers and 22 children 3 mothers / 22 children
        • evolutionary building - genetic algorithm - solution for 6 mothers and 42 children 6 mothers / 42 children
        • evolutionary building - genetic algorithm - solution for 5 mothers and 35 children 5 mothers / 35 children

        • 4 mothers / 30 children - chromosome



          (Y- (F (F Y+ F (Y+ F)) (Y+ F F F (Y- F)) (F Y- F F (Y- F)) (Y+ Y+ F F (F Y+ F)) (Y- F Y+ F F (F Y- F)) (Y+ F Y+ F F (Y+ F F)) (Y+ F F Y- F (Y- F Y- F F)) (Y+ Y+ F Y+ F F (Y+ F Y- F Y- F Y- F))) Z Y- Y- (F (Y- F Y+ F F (Y- F)) (Y+ F F Y- F (F)) (Y+ F F F (Y+ F)) (Y+ Y+ F Y+ F F (F)) (F F (Y+ F)) (F Y- F F (F)) (Y+ Y+ F Y+ F Y- F (F Y- F)) (Y+ Y+ F Y- F Y+ F (Y- F Y- F Y+ F Y+ F))) Z Y- (F (Y- F F F (F)) (Y- F Y+ F F (Y- F)) (Y+ Y+ F F F (Y- F)) (Y+ Y+ F F Y+ F (F)) (F F Y+ F (F Y- F)) (Y+ Y+ F Y- F Y+ F (F Y+ F)) (Y- F F Y+ F (Y- F Y+ F)) (Y+ F Y- F (Y+ F F)) (Y- F Y- F Y+ F (Y- F Y+ F Y+ F Y- F))) Z Y- (F (Y+ F F F (F)) (Y- F F Y+ F (F)) (Y+ Y+ F Y+ F Y- F (F)) (Y- F F F (Y+ F)) (F Y- F Y+ F (F)) (F Y+ F Y- F (Y- F)) (Y+ Y+ F F (Y+ F)) (F Y+ F F (Y+ F)) (Y+ Y+ F Y+ F F (Y- F Y- F)) (Y+ F Y+ F F (Y- F Y- F Y+ F Y+ F))) Z (F (Y+ F Y- F F (Y- F)) (Y- F Y- F Y- F (F)) (Y- F Y- F F (Y- F)) (Y- F Y+ F Y- F (Y- F)) (Y+ F F F (Y+ F)) (Y+ F F Y+ F (F F)) (F Y- F Y+ F (Y- F F)) (Y- F F F (Y- F Y+ F Y+ F Y+ F))))

        • 3 mothers / 22 children - chromosome



          (Y+ (F (Y+ F F (Y+ F)) (F Y- F (Y+ F)) (F Y- F F (Y- F)) (Y+ Y+ F F F (F Y+ F)) (Y- F F Y- F (Y- F Y+ F)) (F Y+ F F (Y+ F Y+ F)) (F F (F Y- F F)) (Y+ Y+ F Y- F F (Y+ F Y- F Y- F Y- F))) Z Y+ Y+ (F (Y+ Y+ F Y+ F F (F)) (F Y+ F F (Y- F)) (F F (Y- F)) (F Y- F F (Y- F)) (Y+ F F F (Y- F)) (Y+ F Y- F F (Y+ F)) (Y- F F (F F)) (Y+ Y+ F F F (Y- F Y+ F Y+ F Y- F))) Z Y- (F (Y+ F F (F)) (Y- F Y- F (Y- F)) (Y+ F Y+ F Y+ F (Y+ F)) (Y+ F Y+ F Y- F (Y+ F)) (Y+ F Y- F Y+ F (Y+ F)) (F Y+ F Y- F (Y- F F)) (Y+ F Y+ F F (Y+ F Y- F)) (F Y- F F (Y- F Y+ F Y+ F Y- F))) Z (F (Y+ F Y- F F (F)) (Y+ Y+ F F (F)) (F Y+ F F (Y- F)) (F Y- F Y+ F (Y- F)) (Y+ F Y+ F F (Y- F Y+ F)) (Y- F F (Y+ F Y+ F)) (F F (F F)) (Y+ Y+ F Y+ F (Y+ F Y- F)) (Y+ F F (Y+ F Y- F Y- F Y- F))))

        • 6 mothers / 42 children - chromosome



          (Y- (F (Y+ Y+ F Y- F F (Y- F)) (Y- F Y+ F Y- F (F)) (F F F (Y- F)) (Y+ F F F (F Y- F)) (Y+ Y+ F Y+ F F (Y- F Y- F)) (Y+ Y+ F Y- F Y+ F (Y- F Y+ F)) (Y- F Y+ F F (F Y- F Y- F)) (Y+ F F Y- F (Y+ F Y- F Y+ F Y- F))) Z Y- (F (Y- F Y- F Y- F (F)) (Y- F F (Y+ F)) (F Y+ F Y- F (Y- F)) (Y+ F Y+ F (Y+ F Y+ F)) (Y- F Y+ F Y- F (Y+ F Y+ F)) (Y+ F F F (F F)) (Y+ F F Y+ F (Y- F Y+ F Y+ F Y- F))) Z Y- (F (Y+ F Y+ F F (Y+ F)) (Y+ F F Y- F (F)) (F F F (Y- F)) (Y- F Y+ F Y- F (Y- F)) (Y+ Y+ F Y- F F (Y+ F)) (Y- F Y- F F (Y- F F)) (Y+ F Y- F (F Y- F)) (Y+ Y+ F Y+ F F (Y+ F Y- F Y- F Y+ F))) Z Y+ (F (Y+ F Y+ F F (Y+ F)) (Y+ F F Y- F (F)) (F F F (Y- F)) (Y- F Y+ F Y- F (Y- F)) (Y+ F F F (Y- F)) (Y- F Y- F F (Y- F F)) (Y+ F Y- F (F Y- F)) (Y+ Y+ F Y+ F F (Y+ F Y- F Y- F Y+ F))) Z Y+ Y+ (F (F F (Y- F)) (Y- F F Y- F (Y+ F)) (Y+ Y+ F Y+ F Y- F (F)) (Y+ F Y+ F Y- F (Y- F)) (F Y- F Y+ F (F F)) (Y+ F Y- F F (Y+ F F)) (Y- F F Y+ F (Y+ F Y- F)) (Y+ F Y+ F F (Y- F F)) (Y- F F F (Y- F Y+ F Y+ F Y- F))) Z Y- (F (Y+ Y+ F F F (Y+ F)) (Y- F Y+ F F (F)) (Y+ Y+ F F Y- F (F)) (Y+ F Y- F Y+ F (Y- F)) (Y- F F F (F)) (Y+ Y+ F F Y+ F (F)) (Y- F F Y+ F (Y- F)) (Y+ F Y+ F Y- F (F)) (F F (Y+ F Y- F)) (Y- F Y- F Y+ F (Y- F Y+ F Y+ F Y- F))) Z (F (Y- F F Y+ F (Y+ F)) (Y+ Y+ F Y- F F (Y- F)) (Y+ F F Y- F (Y+ F)) (Y+ Y+ F Y+ F F (Y- F)) (Y- F F F (Y+ F F)) (Y+ Y+ F F Y- F (Y- F F)) (F Y- F F (Y- F Y+ F Y+ F Y- F))))

        • 5 mothers / 35 children - chromosome



          (Y- (F (F Y+ F Y+ F (F)) (Y- F Y- F F (Y- F)) (F Y- F Y+ F (F)) (Y+ Y+ F F Y- F (Y- F F)) (Y+ Y+ F F F (Y- F Y- F)) (F F (Y- F Y- F)) (F Y- F F (F Y+ F F)) (Y- F Y- F Y+ F (Y- F Y+ F Y+ F Y- F))) Z (F (F Y+ F Y- F (F)) (Y+ F F Y- F (Y+ F)) (Y+ Y+ F Y+ F F (F)) (Y+ Y+ F Y- F F (Y+ F)) (Y+ Y+ F F F (Y+ F)) (Y- F Y+ F F (F)) (Y+ F Y+ F F (F)) (Y- F F Y+ F (F)) (Y- F F F (Y+ F F)) (Y- F Y- F F (Y+ F Y- F Y- F Y+ F))) Z Y+ (F (F Y+ F (Y+ F)) (Y- F F F (Y- F)) (Y+ Y+ F Y- F Y- F (Y- F)) (F F (F Y- F)) (Y+ Y+ F Y+ F F (Y- F Y- F)) (Y+ Y+ F Y+ F Y- F (F F)) (F Y- F Y+ F (Y- F Y- F Y+ F Y- F))) Z Y+ Y+ (F (Y+ F Y+ F F (Y- F)) (F F (Y+ F)) (Y+ Y+ F Y+ F F (Y- F)) (Y+ F F F (Y- F)) (F Y- F Y- F (Y- F)) (F Y- F Y+ F (Y- F Y+ F)) (Y+ Y+ F F (F Y+ F)) (Y+ F F Y- F (Y+ F Y- F Y+ F Y- F))) Z Y- (F (Y+ F Y+ F (Y- F)) (F Y+ F Y- F (Y+ F)) (Y+ Y+ F F Y+ F (F)) (Y+ Y+ F Y+ F F (Y+ F)) (F F (Y+ F)) (Y+ F Y- F Y+ F (F)) (Y+ F F (Y+ F Y- F)) (Y+ Y+ F Y+ F Y+ F (Y- F Y+ F Y+ F Y+ F))) Z (F (F Y+ F Y- F (F)) (Y+ F F (Y- F)) (F F (Y- F)) (Y+ Y+ F Y- F F (Y+ F)) (Y+ Y+ F F F (Y+ F)) (Y- F Y+ F F (F)) (Y+ F Y+ F F (F)) (F Y+ F F (Y- F)) (Y- F F F (Y+ F F)) (Y- F Y- F F (Y+ F Y- F Y- F Y+ F))))


  • floor


    plans
    • oprhanage - evolutionary building - genetic algorithm - floor plans
      •  

        The concept of the design brief is meant to achieve maximum similarity of lifestyle of orphans and children who have parents and encourage children to spent the day playing together.
        They are supposed to take part in the housekeeping activities and attend normal schools and kindergartens.
        The area of common spaces is relatively bigger than the one of bedrooms, offering various games that need more room (table tennis, foosball tables, toy-railways etc.).
        If the main computer, generating the floor plans of the building, finds that this concept does not work, it is able to change the brief (rooms for two children occupy two cells not one, the common space is reduced, particular rooms are removed and new are presented). This intelligence of the building prevents the building from becoming morally aged.



  • technical


    solutions
        • evolutionary building - genetic algorithm - cell
        •  

          Carbon fibre is chosen for load-bearing material. It is approximately three times as strong as steel and about four times lighter, it is no chance that the F1 bolides are made of it. Liquid crystal is proposed for the external walls, giving the possibility of variable colors and patterns.
        • 15 puzzle

           

          The principle of cell motion is inspired by the puzzle shown in the pictures (15 puzzle). The missing element gives the possibility of arranging the others in all possible combinations. The goal is to achieve the state shown in the left picture. In the proposed building the motion is possible in 3D. To move a cell upward, the horizontal rails must be unhooked. In the example below, the stages of the motion of a chosen cell is shown.
        • evolutionary building - genetic algorithm - cell motion principle principle of cell motion
        • evolutionary building - genetic algorithm - cell sliding principle - screws cell sliding principle
        • evolutionary building - genetic algorithm - cell sliding principle cell sliding principle
        • evolutionary building - genetic algorithm - unfolding stairs principle unfolding stairs principle
        • evolutionary building - genetic algorithm - unfolding stairs principle unfolding stairs principle
        • evolutionary building - genetic algorithm - unfolding stairs principle unfolding
        • evolutionary building - genetic algorithm - unfolding stairs principle unfolding
        • evolutionary building - genetic algorithm - unfolding stairs principle unfolding
        • evolutionary building - genetic algorithm - unfolding stairs principle unfolding
        • evolutionary building - genetic algorithm - section elevation section
        • evolutionary building - genetic algorithm - section elevation section
        • evolutionary building - genetic algorithm - 3d section
        • evolutionary building - genetic algorithm - 3d section

  • intro
    • oprhanage - evolutionary building - genetic algorithm - info
      •  

        The nature of this project is experimental and its main purpose is to develop a method for achieving an evolutionary building. The particular example, which is presented, demonstrates one of the possible applications of this method.
        The Evolutionary building in this case is an independent and autonomous organism, which is able to change in time and space without explicit human intervention.
        The aspects of architecture which can be a subject of evolution are various: shape, structure, texture, even stylistics. Since the beginning of the project it becomes clear that achieving evolution of all building aspects is impossible and concentration on one of them is necessary.
        The functional structure of the building has been chosen, so that it is enough for demonstrating the evolution.
        Probably the great benefits of such intelligent and adjustable architecture will be explored in several decades, but it does not mean that ideas and developments on this topic are useless nowadays.