‘When I was a lad, all this was fields’, in a manner of speaking. Bio-
informatics is a young discipline, and I suspect my background and
history have made me an unconventional practitioner. It’s a funny old
game, bioinformatics, as is science itself, especially to someone who
comes at it sideways.
I’m old enough now to have seen fashions come, and go, and be
revived. I first encountered a widely available, easy-to-program, object-
oriented user interface development toolkit with the release in the late
1980s of Apple’s HyperCard, and I used its elegant HyperTalk script-
ing language for my first attempts at programming using genetic
algorithms.
These make use of models of evolutionary operators – such as repro-
duction, mutation, crossover, and selection by fitness – applied to a
population of candidate solutions. Some papers have been published
exploring the applicability of genetic algorithms to sequence assembly.
Other interesting work has focused on genetic programming, where the
aim is to evolve not just a solution, but the program code to solve a
problem.
I have been considering evolutionary strategies in application
development – if you like, the other side of adaptive programming,
the one that doesn’t involve typing. At its simplest, this just means
appreciating that software is created, grows, interacts, and adapts or is
adapted to changing circumstances, like any other thing that exists.
My interest in how genetic algorithms can be used to evolve optimal
solutions to complex and changing multi-parameter problems has also
provided an interesting framework for observing how our customers
in the world of bioinformatics application development – the scientists
– actually do science.
One can look at the scientists in a particular field in terms of a
population of individuals, each of whom is adapted to their circum-
stances to a greater or lesser extent.
Those individuals who are best suited to the prevailing conditions
will do better than others; their fitness, measured by such factors as
publication record, history of successful collaboration, development of
innovative techniques, or ability to attract external funding, will result
in their becoming more influential in the field.
However, circumstances can rapidly change, and characteristics that
are valuable in one situation can become liabilities in another. Thus,
over time, the population of scientists as a whole demonstrates differ-
ent features in accordance with the changing demands of their
environment.
HYPERCARD, GENETIC ALGORITHMS, EVOLUTIONARY DEVELOPMENT 49