• People involved
• About Evolutionary Computation
COMP2SYS people involved in evolutionary
computation research are:
Senior scientists involved in evolutionary computation research
- Anders Christensen
- Giovanni Pini
- Marco Dorigo
- Hugues Bersini
- Mauro Birattari
- Elio Tuci
- Tom Lenaerts
- Thomas Stützle
About Evolutionary Computation
Evolutionary computation (EC) algorithms
are inspired by nature's capability to evolve living beings
well adapted to their environment that cooperate or compete
with other members of the population. EC algorithms can be
characterized as computational models of the evolutionary
process that take inspiration from the natural genetic variety
and natural selection.
At each iteration of the EC algorithm a number
or operators is applied to the individuals of the current
population to generate the individuals of the population of
the next generation (iteration). Usually, EC algorithms use
operators called recombination or crossover
to recombine two or more individuals to produce new ones.
They also use mutation or modification operators
which cause a self-adaptation of individuals. The driving
force in EC algorithms is the selection of individuals
based on their fitness (which can be based on the
objective function or some other kind of quality measure).
Individuals with a higher fitness have a higher probability
to be chosen as members of the population of the next iteration
(or as parents for the generation of new individuals). This
corresponds to the principle of survival of the fittest
in natural evolution.
There has been a variety of different EC
algorithms proposed over the years. Basically, they fall into
three different categories which have been developed independently
of each other. These are evolutionary programming
(Fogel ; Fogel et al. ), evolution strategies
(Rechenberg ; Schwefel ), and genetic algorithms
(Holland ; Goldberg ; Mitchell ).
Over the years there have been quite a few
overviews and surveys about EC methods. Among these are the
ones by Bäck , by Fogel , by Spears et al.  and by
Michlewicz and Michalewicz . In Calegari et al.  a taxonomy
of EC algorithm is proposed.
Despite their superficial differences, the three main approaches
to EC share the same basic template. A general outline of
an EC algorithm is shown here:
Framework for Evolutionary Computation
Generate initial population P(0)
t <- 0
while termination conditions not met do
P'(t) <- Select(P(t))
P''(t) <- ApplyReproductionOperators(P'(t))
P(t+1) <- Replace(P(t), P''(t))
t <- t+1
 L. J. Fogel. Toward inductive inference
automata. In Proceedings of the International Federation
for Information Processing Congress, pages 395-399, Munich,
 L. J. Fogel, A.
J. Owens, and M. J. Walsh. Artificial Intelligence through
Simulated Evolution. John Wiley & Sons, New York,
 I. Rechenberg.
Evolution strategy: Optimization of technical systems
by means of biological evolution. Fromman-Holzboog, Stuttgart,
 J. H. Holland.
Adaptation in natural artificial systems. University
of Michigan Press, Ann Arbor, MI, 1975.
 T. Bäck. Evolutionary
Algorithms in Theory and Practice: Evolutionary Strategies,
Evolutionary Programming, Genetic Algorithms. Oxford
University Press, New York, NY, 1996.
 D. B. Fogel. An
introduction to simulated evolutionary optimization. IEEE
Transactions on Neural Networks, 5(1):3-14, Jan 1994.
 W. M. Spears,
K. A. De Jong, T. Bäck, D. B. Fogel, and H. de Garis. An overview
of evolutionary computation. In P. B. Brazdil, editor, Proceedings
of the European Conference on Machine Learning (ECML-93),
volume 667 of Lecture Notes in Artificial Intelligence,
pages 442-459, Berlin, Germany, 1993. Springer-Verlag.
 Z. Michlewicz
and M. Michalewicz. Evolutionary computation techniques and
their applications. In Proceedings of the IEEE International
Conference on Intelligent Processing Systems, pages 14-24,
Piscataway, NJ, 1997. IEEE Publications.
 P. Calegari, G.
Coray, A. Hertz, D. Kobler, and P. Kuonen. A taxonomy of evolutionary
algorithms in combinatorial optimization. Journal of Heuristics
, 5:145-158, 1999.
 H.-P. Schwefel.
Numerical Optimization of Computer Models. John Wiley
& Sons, Chichester, UK, 1981.
 D. E. Goldberg.
Genetic Algorithms in Search, Optimization and Machine
Learning. Addison-Wesley, Reading, MA, 1989.
 M. Mitchell.
An Introduction to Genetic Algorithms. MIT Press,
Cambridge, MA, 1996.
• The Evonet (European Network of Excellence in Evolutionary Computing) website