### Genetic algorithm

A genetic algorithm is a stochastic search method based on natural selection and reproduction of a population of individuals.

Each generation, the fittest candidates in the population are selected, paired, and recombined into a new generation. With each new generation the system converges towards an optimal fitness.

Optimal fitness can be global or local:

- The global optimum is the optimal solution among all possible solutions; the GA's goal.
- The local optimum occurs when the GA converges too fast and gets trapped.

Local optima occur when the population is no longer diverse enough. Therefore, we need to preserve some weaker candidates whose role can become more prominent later on. A detailed paper on optima and hierarchical fair competition (Jianjun Hu et al.) is here.

## Grid symmetry example

Let's have a look at an example. During the 2009 NodeBox workhop in Finland (Lahti University of Applied Sciences, Institute of Design) Jonatan Hilden and I composed a simple genetic algorithm for NodeBox that we wanted to share with you.

To make it work, you have to code three things yourself:

**Candidate:**the members in a population that are going to improve through evolution**Recombination function:**what candidate comes out of the crossing of two parent candidates?**Fitness function:**calculates the "score" of a candidate.

We've included a *grid candidate* with black and white squares that will strive for symmetry.

In the image below you can see how generations of grids evolve over time:

If you look at the source code of the grid candidate closely you'll see that it is just a list of *True* or *False* switches (*True* means black square in the grid, *False* means white square in the grid). The candidate furthermore has some helper methods to find out what squares surround the current one. These are used in the fitness function to discover symmetrical patterns.

Grids that have more symmetry (whose black squares have a horizontal and/or vertical reflection) score better, and will therefore be able to reproduce parts of their DNA (the list of *True*/*False* values) more often.

## Source code

Naturally, a candidate can be many other things beside a grid of black and white squares (a creature, source code, ...) A fairly easy first experiment would be to adapt the grid's DNA list to work with many colors instead of just black and white.

# A genetic algorithm is a stochastic search method based on # natural selection and reproduction of a population of individuals. # Each generation, the fittest candidates in the population are selected, # paired, and recombined into a new generation. # With each new generation the system converges towards an optimal fitness. # Optimal fitness can be global or local. # - The global optimum is the optimal solution among all possible solutions; # the goal of the the GA. # - The local optimum occurs when the GA converges too fast and is trapped. # This occurs when the population is no longer diverse enough. # Therefore, we need to preserve some weaker candidates whose role # can become more prominent later on. # - Stochastic: the process of selecting the fittest candidates in # the population involves an amount of randomness, so weaker candidates # are sometimes preserved. # - Convergence: the approach toward a definite value or equilibrium state. # We can use some extra computing power: try: import psyco psyco.full() except: pass ### GENETIC ALGORITHM FUNCTIONS ########################################## def candidate(): # Needs to be implemented. return None def recombine(a, b, crossover=0.5): # Needs to be implemented. return None def fitness(candidate): # Needs to be implemented. return 0 def population(size=500): """ Returns an initial list of random candidates (e.g. generation 0). """ return [candidate() for i in range(size)] def sort_by_fitness(candidates): """ Returns a sorted list of (fitness, candidate)-tuples; best-first. """ return sorted([(fitness(x), x) for x in candidates], reverse=True) def select(population, top=0.7, determinism=0.8): """ Returns a selection of fit candidates from the population. - top: roughly the fittest 70% candidates are allowed to reproduce. - determinism: there is a 20% chance that good candidates are ignored, this keeps the population diverse. """ population = sort_by_fitness(population) population = [candidate for (fitness, candidate) in population] parents = list(population) i = len(parents) while len(parents) > len(population)*top: i = (i-1) % len(parents) if random() < determinism: parents.pop(i) return parents def reproduce(population, top=0.7, determinism=0.8, crossover=lambda: random()): """ Returns a new population of candidates. Selects parent that are fit to reproduce. Recombines random pairs of parents to new child candidates. """ parents = select(population, top, determinism) children = [] for i in range(len(population)): i = random(len(parents)) j = choice( range(0,i) + range(i+1, len(parents)) ) k = crossover try: k = k() except: pass children.append(recombine(parents[i], parents[j], crossover=k)) return children def converged(population): """ Returns True when the population has reached its optimum. """ for i in range(1, len(population)): if population[i-1] != population[i]: return False return True ### GRID CANDIDATE ####################################################### # Any kind of candidate can be used in the GA. # Here's an example of random black & white grids that score better # as they become more symmetrical. # We just create one candidate (or agent) with a fitness property and # the capability to recombine; the GA functions will create a population # and keep improving it until it converges. # Internally, the grid is just a list of colors (in this case: True (black) # or False (white)). This makes it an ideal candidate to work with: # lists are easy to examine and cut-and-splice in the recombine function. # You can imagine how the values in the list could encompass a wider range # of colors(for example: 0=black, 1=white, 2=red, ...) class GridCandidate(list): def __init__(self, rows, cols, values=[]): self.rows = rows self.cols = cols # Cells in the grid are randomly black (True) or white (False). if len(values) == 0: values = [choice((True, False)) for i in range(rows*cols)] list.__init__(self, values) def draw(self, x, y, scale=10.0): for i in range(self.cols): for j in range(self.rows): is_black = self[i+j*cols] _ctx.fill(int(not is_black)) _ctx.oval(x+i*scale, y+j*scale, scale, scale) # Triangles: #R = 1.11803398875 # equilateral width/height ratio #_ctx.push() #_ctx.translate(x+i*scale/2, y+j*scale/R) #if i % 2 == int(j & 2 == 0): # up for odd col in even row # _ctx.beginpath(0, 0) # _ctx.lineto(scale/2, scale/R) # _ctx.lineto(scale, 0) #else: # _ctx.beginpath(0, scale/R) # _ctx.lineto(scale/2, 0) # _ctx.lineto(scale, scale/R) #_ctx.endpath() #_ctx.pop() def contains(self, i): return i > 0 and i < len(self) def row(self, i): return i / self.cols # the row index i is in def col(self, i): return i % self.cols # the column index i is in def reflect(self, i, axis="horizontal"): """ Returns the index in the grid that is symmetrical to this one. For example: 0 1 2 3 4 5 6 7 8 9 reflect(1, "horizontal") => 3 reflect(4, "vertical") => 9 """ if axis == "horizontal": return self.row(i)*self.cols + self.cols - self.col(i) - 1 if axis == "vertical": return (self.rows-self.row(i)-1)*self.cols + self.col(i) def recombine(self, other, crossover=0.5): i = int(len(self) * crossover) return GridCandidate(self.rows, self.cols, values=self[:i]+other[i:]) @property def fitness(self): # Fitness is calculated in terms of symmetry. # Grids with the same color at symmetrical positions score better. score = 0 for i in range(len(self)): j = self.reflect(i, "horizontal") if self.contains(j) and self[j] == self[i]: score += 1 j = self.reflect(i, "vertical") if self.contains(j) and self[j] == self[i]: score += 1 return score # Here are some functions you may want to use to determine fitness: def above(self, i) : return i-self.cols def below(self, i) : return i+self.cols def left( self, i) : return i+1 def right(self, i) : return i-1 north, south, east, west = above, below, left, right def northeast(self, i) : return self.north(self.east(i)) def northwest(self, i) : return self.north(self.west(i)) def southeast(self, i) : return self.south(self.east(i)) def southwest(self, i) : return self.south(self.west(i)) # Grid size. rows = 5 cols = 5 candidate = lambda: GridCandidate(rows, cols) recombine = lambda a, b, crossover: a.recombine(b, crossover) fitness = lambda candidate: candidate.fitness # The initial random population: p = population(size=500) size(800, 2000) # Process n generations: for i in range(40): p = reproduce(p, top=0.6, determinism=0.65) #translate(0, 35) #push() #for candidate in p[:15]: # translate(35, 0) # candidate.draw(0, 0, scale=5) #pop() if converged(p): print "converged at generation ", str(i) break # A list of scores for each member in the final population: #print [score for score, x in sort_by_fitness(p)] # The best solution in the final population: p[0].draw(20, 20, scale=30)

Created by Jonatan Hilden and Tom De Smedt

Jonatan will be doing his thesis at the Experimental Media Group and is working on a library for recombinable vector artwork.