begin with random population
repeat 
    selection
    crossover
    mutation
    ==> new population
until fitness of best individual is optimized

Fitness Function

• Function F defined for every chromosome (of length l)
• Interpretation: F(c₁) < F(c₂) implies c₁ is less fit than c₂
• Example: F(c) = number of 1s in c

Fitness-Proportionate Selection

• Called viability selection in biology
• Implementation: roulette wheel sampling
• Each individual of population assigned a sector of wheel proportional to it's fitness
• Spin wheel to select an individual

begin with random population
repeat 
    selection
    crossover
    mutation
    ==> new population
until fitness of best individual is optimized

Crossover

• With probability p_c, cross two parents at a randomly selected location
• Example of crossover, at location 3:

  parent pair   x3    child pair
    11101100          11111011
    10111011          10101100
  

• p_c = 0.4 - 0.7 (typical)

begin with random population
repeat 
    selection
    crossover
    mutation
    ==> new population
until fitness of best individual is optimized

Mutation

• With probability p_m, mutate at each locus
• For binary alphabet, mutation is a bit flip
• p_m = 0.001 (typical)

Fitness Optimization Criteria

• Stop when "close" to perfect
• Weak link: cannot know how close or far away "perfect" is
• Practice: Take many runs, vary parameters, keep record of best (c, f(c)) for each run.

Algorithm

main
{
  input
  {
    L,   // chromosome length
    N,   // population size // make this an even number for convenience
    P,   // randomly generated set of n chromosomes of length L
    p_c, // crossover probability
    p_m, // mutation probability
    F    // fitness function
  }
  max_fitness = maximum value of F(c) for c in P;
  c_max       = chromosome in P where maximum is achieved;
  while (max_fitness < fitness_goal)
  {
    P1          = generate_new_population(P);
    max_fitness = maximum value of F(c) for c in P1;
    c_max       = chromosome in P1 where maximum is achieved;
    P           = P1;
  }
  output
  {
    P,
    c_max,
    F(c_max)
  }
}

generate_new_population(P)
{
  for (i = 0; i < N/2; ++i)
  {
    choose two elements c1, c2 of P using roulette wheel sampling;
    with probability p_c, crossover(c1,c2);
    mutate (c1); mutate (c2);
    insert c1 and c2 in P1;
  }
  return P1
}

crossover (&c1 &c2) // passed by reference
{
  loc = random [0,L);
  for (i = 0; i < loc; ++i)
    c1[i]=c2[i];
  for (i = loc; i < L; ++i)
    c2[i] = c1[i];
}

mutate (&c) // passed by reference
{
  for (i = 0; i < L; ++i)
    with probability p_m flip c[i];
}

Example

L    = 8
N    = 4
p_c  = 0.6
p_m  = 0.1
P    = { 00000110 , 11101110 , 00100000 , 00110100 }
F(c) = number of '1'

  P[0]   F ROULETTE XC   P[1]   F ROULETTE XC   P[2]   F ROULETTE XC   P[3]   F 
-------- - -------- -- -------- - -------- -- -------- - -------- -- -------- - 
00000110 2 11101110 y3 11110100 5 11101110 y5 11101100 5 11110110 y6 11110110 6
11101110 6 00110100    00101110 4 11110100    11110110 6 11101110    11101110 6 
00100000 1 11101110 n  11101110 6 11101110 y2 11101110 6 00101110 y4 00100110 3
00110100 3 00000110    00000110 2 00101110    00101110 4 11110110    11111110 7

ROULETTE XC   P[5]   F ROULETTE XC   P[6]   F ROULETTE XC   P[7]   F 
-------- -- -------- - -------- -- -------- - -------- -- -------- - 
11111110 y2 11110110 6 11111110 n  11111110 7 11111110 y3 11111110 7
11110110    11111110 7 11111110    11111110 7 11111110    11111110 7
11111110 n  11111110 7 11101110 y7 11111110 7 11111110 y7 11111110 7
11101110    11101110 6 11111110    11101110 6 11101110    11101110 6

• Note that P[7] = P[6]
• From P[7], two "bad" genes are propagated without possibility of change by crossover: sites 4, 8
• The site 4 can be removed only by the failure of the individual to propagate - which is likely to happen eventually because of roulette wheel sampling. (Possible, but much less likely, is that the other genotype will die out.)
• Add mutation: Eventually mutation will occur at site 8, producing an optimally fit individual in the population (assuming the unlikely possibility of death of 11111110 has not occured).

Applying GA

• Problem Coding
• Find alphabet and length that represents problem instances
• Reversible mapping {problem instances} <--> {chromosomes}
• Sometimes this is straightforward, sometimes it is a leap, and sometimes the straightforward approach is not the best
• Fitness Function
• Evaluate representative chromosome
• Function F:{chromosomes} --> {numbers}
• F(c) high value ==> instance represented by c is good

Theory

• Schemata - chromosomes with "wild cards" - 10*0*1110*
• Order of a schema H o(H) = number of fixed positions
  o(10*0*1110*) = 7
• Defining length of a schema H d(H) = distance between first and last fixed position
  d(10*0*1110*) = 9
• m(H,t) = number of representatives of H in population at time t
• f(H) = average fitness of chromosomes in H
• f(P) = average fitness over entire population P

Schema Theorem (Holland, 1975)

m(H, t + 1) >= m(H,t) [f(H)/F(P)] [1 - p_c d(H)/(l - 1) - o(H)p_m]

Standard Interpretation: The number of representatives of a schema in a population grows in proportion to the fitness of the schema. This interpretation has come into question in recent years. The theory of GA in particular and evolutionary computation in general is an active field of research.

Variations

• "Fitness" may be inversely related to F(c) (low is good) - rescale for Roulette
• Crossover often needs coding-specific definition or exceptions
• Multipoint crossover
• Variable mutation rate (inversely proportional to Hamming distance between parents)

Representative Application Areas

• Artificial Life
• Lisp Programs
• Cellular Automata
• Real Life
• Learning and survival: models of Baldwin Effect
• Population biology simulations
• Ecosystems
• Science
• Protein Structure
• Nonlinear Opitimization
• OR, Prediction
• AI
• Evolving Learning Algorithms: backprop parameters; reinforcement learning, others
• Neural Network Architecture

Evolving Neural Architectures

• Direct Encoding
• Problem Mapping:
  adjacency (connection) matrix <==> network topology
  feed-forward network <==> lower-diagonal matrix
  alphabet = {0,1}
  chromosome = rows of adjacency matrix concatenated
• Crossover:
  exchange rows
  row = input to a single node
• Mutation: standard
• Fitness: for a given problem,
  backprop for a fixed number of epochs using training set
  evaluate on test set
  sum square error = fitness (low is good)
• Montana & Davis
• Grammatical Encoding
• Problem Mapping:
  graph generation grammar ==> graph
  alphabet = set of non-terminals in grammar
  chromosome = string of non-terminals (first element must be S = "start")
• Crossover: standard or multiple-site
• Mutation: variable rate (inverse Hamming)
• Fitness: train, test as above
• Hiroaki Kitano, [Former Director, SONY Computer Science Lab], Drug Design
  Kitano Publications

Grammatical Encoding - Detailed Example

Alphabet = {S,A,B,C,D,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p}

Chromosome = SXXXX XXXXX XXXXX XXXXX XXXXX ...
(X = any alphabet character) (spaces for visual convenience only)

Grammar Terminals (fixed):

a -> 0 0    b -> 0 0    c -> 0 0    d -> 0 0    e -> 0 1 
     0 0         0 1         1 0         1 1         0 0   

f -> 0 1    g -> 0 1    h -> 0 1    i -> 1 0    j -> 1 0 
     1 0         0 1         1 1         0 0         0 1

k -> 1 0    l -> 1 0    m -> 1 1    n -> 1 1    o -> 1 1
     1 0         1 1         0 0         0 1         1 0

p -> 1 1
     1 1

Example non-terminals (variable):

S -> A B    A -> c a    B -> a a    C -> f g    D -> c a
     C D         f a         a a         o k         p c

represented by chromosome SABCDAcafaBaaaaCfgokDcapc

Example chromosome SABCD Aaaca Baaaa Cokab Daaoa
decodes as follows:

S -> A B -> a a a a -> 0 0 0 0 0 0 0 0  = adjacency matrix
     C D    c a a a    0 0 0 0 0 0 0 0
            o k a a    0 0 0 0 0 0 0 0  
            a b o a    1 0 0 0 0 0 0 0
                       1 1 1 0 0 0 0 0
                       1 0 1 0 0 0 0 0
                       0 0 0 0 1 1 0 0
                       0 0 0 1 0 1 0 0

Need ad hoc rules such as:

• Take first production for each non-terminal, discard the rest
• Discard backward connections (make matrix lower diagonal)
• discard intra-layer connections

Research Area: Applying GA to Acyclic Architectures

• Terminals - use with known interesting subnets (e.g., Expert Net Nodes)
• Matrix Blocks
• Applicable to Neural or Computational Nets
• Fitness - possibly penalize for number of connections, selecting for sparseness

References

• Golgberg, David E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
• Mitchell, Melanie, An Introduction to Genetic Algorithms, MIT Press, 1996
• NRL GA Archives
• Artificial Life Home Page
• Lacher, R.C., Expert networks: Paradigmatic conflict, technological rapprochement, Minds and Machines 3 (1993) 53--71