Evolutionary Design of Single-Mode Microstructured Polymer Optical
Fibres using an Artificial Embryogeny Representation
Steven Manos, Maryanne C. J. Large, Leon Poladian
Using Genetic Algorithms the authors were able to create a new design for Microstructured
polymer optical fibres (MPOF) that can overperform human designs.
The paper won the 2007 “HUMIES”, a competition for emerging applications of EA
technologies that can be "competitive with the work of creative and inventive humans", and
was presented at GECCO 2007 (Genetic and Evolutionary Computing Conference).
The problem:
Determine hole patterns for optical fibers. Good patterns can increase the bandwith.
The previous pattern, created by experts, was an exagonal pattern.
Using Genetic Algorithms new (and better) patterns have been “invented”.
Some details:
The position and size of the holes is coded in a “chromosome” that contains the binary
representation of:
 symmetry number ( given a pattern how many times it have to be repeated around the
 holes positions and sizes
Each design pattern is coded in a variable length binary string, that generate, using a process
called embryogenesis, a complex pattern.
An initial population of random generated strings are recombined using a model of Darwinian
natural evolution.
Each “individual” is evaluated with a fitness function (Elitist scheme + Pareto Ranking ), and
the best are used to give birth to a new generation of strings.
Optical fibres with optical characteristics that are competitive with pre-existing products on the
Designs with novel symmetries and hole patterns, and fewer holes.
‘GIMP 3’
Some discussion points…
All the best designs show no 6-symmetry, like in the human designed solution.
Have the machines “invented” a new design? What is invention?
This process requires a big amount of CPU time, mostly for the computation of the fitness: to
evolve 200 generations it takes 70 days (10 minutes for each individual evaluation). Using
parallel computation techniques it takes “only” 2 - 4 days.
Is this process resulted in a exhaustive search in the solution space?
Using a Genetic Algorithms approach we have a result but we don’t know how the machine
has arrived at that result.
Is this a problem? We can still sell or patent that result?
The authors of the book Genetic Programming IV: Routine Human-Competitive Machine
Intelligence propose:
“We define the AI ratio (the “artificial-to-intelligence” ratio) of a problem-solving method as the
ratio of that which is delivered by the automated operation of the artificial method to the
amount of intelligence that is supplied by the human applying the method to a particular
In this case how much intelligence have been given by humans ( representation of the
problem, fitness function…)?
Some references and articles…