Hello, I am a researcher in the field of *computational intelligence*.
Currently I am working as a software developer at OM Partners in Antwerp, Belgium.

My research is about *multi-objective optimization* using *evolutionary computation*.
Now, let us disassemble that into smaller pieces. At first, we look at *optimization*.

Suppose that you want to go on a round trip around USA. You select the cities you want to visit and mark them
on a map. Suppose you can fly from one city to another in a straight line. What is the *shortest path*
that visits each city and gets you back to where you started?
Try to connect all the cities using a *shortest* possible route. You choose a starting point by clicking
on an arbitrary city. (My best route is 10572.92 Miles).

- Clear

Seems easy enough. However, if you found the shortest route to this particular problem in 1962, you would win $10 000, which is about $80 000 adjusted for inflation. "So where is the catch? Surely anybody with a computer can just run through all the possible routes." you ask. The catch is that there are 131 565 418 466 846 765 083 609 006 080 000 000 possible tours around these particular 33 cities. If you used a computer which checks a billion solutions per second, it would take 4 171 382 957 097 234 149 years to enumerate all solutions. All this trouble just to plan a trip around 33 cities.

"Surely we can do better than go through all possible solutions." You say. And you're right. And this is
what optimization is all about. The term *optimization* has many meanings in today's world. You could
hear people speaking about optimizing their daily routine, programmers speaking about optimizing their programs
and so on. My research is about *mathematical optimization*. That is the task of constructing a
solution to a mathematical problem, *that is best* in some well defined sense, and hopefully finishing
before the heat death of the universe.

Life is hard. The environment is full of dangers and it is constantly changing. Yet it is full of living organisms which successfully inhabit this planet for billions of years. Each individual living being is a survival expert in their particular domain. For example, the arctic rabbit is a survival expert in the domain of northern Canada. With a little bit of imagination, we can consider each individual arctic rabbit to be nature's solution to the mathematical problem of constructing a living machine capable of surviving in the arctic. Looking at how successful nature is in creating complicated solutions such as the arctic rabbit, humans tried to imitate evolution to solve mathematical problems.

Evolution works on two levels, the *genetic* level, and the *environmental* level. On each level,
there are two fundamental concepts: mating and survival of the fittest at the environmental level, and
crossover and mutation on the genetic level.

In a population of rabbits, there is always competition for mating. Female rabbits prefer partners, whom
they consider strong and *fit*. One way the female rabbits possibly do this is by looking at the white fluffy
tails for signs of diarrhea.^{1} Rabbits with better genes, who have
stronger immunity systems, have a smaller chance to contract diarrhea, and hence a higher chance to be chosen
by the females for mating. Therefore ''better genes'' have a higher chance to proliferate.

When rabbits mate, their DNA is recombined using crossover to create the DNA of a brand new rabbit. The baby rabbit gets some proportion of his genetic code from his father and some from his mother. This way, he may inherit some good or bad traits from his parents. In addition, his DNA is altered a little by random errors in the DNA replication process. The introduction of these errors is called mutation. However, in this article I want to concentrate mainly on crossover.

Once a baby rabbit is born, he has to prove himself, by surviving in the harsh environment. If the offspring rabbit inherited bad genes from his parents, he will have a smaller chance of surviving until maturity and a smaller chance to reproduce and pass these bad genes on. On the other hand, if he inherited good genes, he has a higher chance to have many children himself, and to proliferate his genes further.

Now let us look at how we can use the ideas of evolution in the task of finding the best round trip through the 33 cities. First of all, we need DNA. That is, we need some concise way on how to encode a solution to the road trip problem. One possible way is to give each city a number like this:

Then each road trip can be represented as a sequence of numbers 1 to 33. Now that we have a genetic representation, we can manipulate the individual road trips in interesting ways. Let us take 2 random road trips, one illustrated in blue, and one in red.

Each trip has its own genome, which is illustrated in the picture bellow. Now let us suppose that these two road trips are going to mate, to create children. We need some way in which to combine the red genome and the blue one, so that we get a valid genome of some other road trip around all the cities. For example, we do not want to visit one city twice. Similarly, when constructing rabbits, nature ensures that the resulting rabbit does not have two tails.

One way to do it for the road trip problem is the so-called order crossover.^{2}
The procedure is illustrated in the image above. At first, an arbitrary block of symbols is inherited from the
first parent (the red genome). Next, symbols are inherited one by one from the second parent, but *only*
if they are not already present in the offspring. This ensures that each symbol is present in the offspring exactly once.
Once the genome of the offspring is generated, we can construct the round trip corresponding to this genome:

Here we see a remarkable thing. *The offspring has a shorter length than both of its parents!*
Looking at the image closely, we see the reason behind this. The blue parent makes a lot of unnecessary trips
in the east (right) part of the map. The offspring however does not inherit this faulty sequence, but instead inherits
the eastern part of the trip from the red parent, who performs quite well on this part of the map.
Conversely, the red parent stumbles in the western part of the map, but the offspring inherits the western part
from the blue parent, who fares very well.
However, an offspring does not necessarily inherit the good traits of its parents. If the block inherited
from the red parent was a little different, such as in the following picture:

The resulting offspring would still inherit the good western part from the red parent and the good eastern part from the blue parent, but in a way that would combine them very clumsily. We can see this in the following picture.

This is where the *survival of the fittest* comes into play. Let us suppose that we began with 100 randomly
generated genomes and by mating we generated 100 more. Now, we have 200 individuals. Each producing a round trip
of a different length. What we usually do at this point is to pick the *worst* 100 individuals, that is 100
individuals producing *longest* round trip, and to delete them from the population. This way we ensure that
bad gene sequences do not propagate further.

Coming soon.

Coming soon.

^{1. The Selfish Gene, Richard Dawkins↩}
^{2. Introduction to Evolutionary Computing, Eiben, A.E., Smith, James E.↩}

If you like my CV and would like to do a similar one, check out my BitBucket for the source code. You can use an online Latex compiler, such as https://www.overleaf.com/ to generate the pdf file for you without installing anything.

- An Analysis of Differential Evolution Parameters on Rotated Bi-objective Optimization Functions
- Computational Cost Reduction of Non-dominated Sorting Using the M-front
- Attempt to Reduce the Computational Complexity in Multi-objective Differential Evolution Algorithms
- Comparison of Parameter Control Mechanisms in Multi-objective Differential Evolution
- My PhD thesis