Redundant Genetic Encodings May Not Be Harmful
Bryant A. Julstrom
Department of Computer Science
St. Cloud State University
St. Cloud, MN 56301 USA
julstrom@eeyore.stcloudstate.edu
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In /Proceedings of the Genetic and Evolutionary Computation Conference/
(GECCO'99) (Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H.
Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, Eds.). San
Francisco, CA: Morgan Kaufmann Publishers, 1999, p.791.
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Summary
In a redundant genetic encoding, several distinct chromosomes represent
each candidate solution to the target problem. Such an encoding would
seem to hinder genetic search by allowing competing representations of
the same information. Tests using a GA for the 3-cycle problem (3CP),
which seeks to partition /n/ = 3/k/ points in the plane into 3-cycles of
minimum total length, indicate that this is not necessarily so.
Three encodings of the problem's solutions have much, some, and no
redundancy. All number the target instance's points and represent sets
of 3-cycles as permutations of those numbers. In the most redundant
encoding, the first three points a chromosome lists form a cycle, the
next three points form another, and so on. In this encoding, 6^/n//3
(/n//3)! permutations represent each set of 3-cycles on /n/ points. A
less redundant encoding sorts the integers within each triple, so that
(/n//3)! chromosomes represent each set of 3-cycles. Finally, a
non-redundant encoding also sorts the triples by their first values, so
that the mapping from chromosomes to solutions is one-to-one.
A reasonable crossover for these encodings is Partially Mapped Crossover
(PMX) (Goldberg and Lingle, 1985) which randomly chooses a segment
within one parent, then swaps symbols within that parent until the
segment is identical to the same positions in the second parent.
Mutation can swap the symbols in two random positions of the parent
chromosome.
The three encodings were compared in a generational genetic algorithm
that initializes its population randomly, selects parents in
2-tournaments, and applies PMX and mutation independently. It is
1-elitist and runs through a fixed number of generations.
This GA was run, with each encoding, on six instances of 3CP. Three of
the data sets are well known traveling salesman problem instances. The
others consist of points randomly placed on a 100 by 100 grid. The
number of points used was always a multiple of three.
In these tests, the GA's population contained 2/n/ chromosomes. The
probability that crossover would produce an offspring was 70%; that of
mutation therefore 30%. The GA ran through 10/n/ generations, and it was
run 50 times with each encoding on each test instance. Table 1 shows the
results of these trials. Contradicting conventional expectations, the GA
performed best with the two redundant encodings and worst with the
non-redundant encoding.
*Table 1.* Averages over 50 trials with each encoding.
Problem (/n/) Most
redundant Less
redundant Non-
redundant
rand42 (42) 603.25 608.62 613.68
eil51 (51) 501.63 498.70 503.37
rand60 (60) 679.17 685.80 694.89
eil76 (76) 629.22 629.16 653.45
rand90 (90) 887.95 895.20 927.42
kroA100 (99) 23981.87 24097.53 26796.28
There are several possible explanations for these results. First, the
redundant encodings allow more locality. Second, cross-competition
between building blocks may not hinder the GA's search as much as is
often suggested. Third, with the redundant encodings, the population may
form subpopulations around particular representations of good
sub-solutions; one of these subpopulations eventually controls the
population's evolution. Finally, under PMX, many pairs of redundant
chromosomes are effectively the same.
Reference
D. E. Goldberg and R. Lingle, Jr. (1985). Alleles, loci, and the
traveling salesman problem. In J. J. Greffenstette, ed.,
/Proceedings of the First ICGA/, pp.154-159. Hillsdale, NJ: Lawrence
Erlbaum.