During chemical reactions, molecules interact to produce new molecules. Some of the reaction mechanisms are very close to the way gene combine to produce new genes. Like the classical genetic crossover operator, chemical crossover occurs when two molecules exchange the atomic material they are composed of. Molecules do so in order to produce new and more stable molecules. Several differences exist however between the genetic crossover and its chemical version. Molecules are not coded as binary string but as computational tree instead. Moreover this computational tree, due to the symmetry in the way atoms are connected, must be organized in a strictly ordered way. Following a crossover, the resulting molecules must be reshaped according to precise organization rules. The crossover involves the exchange of single or multiple links. The fitness is distributed through the molecule such that only "better" molecules can result from the crossover. In this paper the chemical crossover and the computer simulation will be discussed within several perspectives: chemical, Alife and engineering. Simulation results will be presented for a simple chemical reactor composed of four atoms with different valence, and allowing molecules to deterministically or randomly interact according to the single-link-crossover.
[1]
Rich Caruana,et al.
Removing the Genetics from the Standard Genetic Algorithm
,
1995,
ICML.
[2]
F. Varela,et al.
Development of an idiotypic network in shape space.
,
1994,
Journal of theoretical biology.
[3]
Hugues Bersini.
Design Patterns for an Object-Oriented Computational Chemistry
,
1999,
ECAL.
[4]
Wolfgang Banzhaf,et al.
Self-Evolution in a Constructive Binary String System
,
1998,
Artificial Life.
[5]
Rodney A. Brooks,et al.
Asynchrony induces stability in cellular automata based models
,
1994
.
[6]
John R. Koza,et al.
Hidden Order: How Adaptation Builds Complexity.
,
1995,
Artificial Life.
[7]
Stuart A. Kauffman,et al.
The origins of order
,
1993
.
[8]
John R. Koza,et al.
Genetic programming - on the programming of computers by means of natural selection
,
1993,
Complex adaptive systems.
[9]
Hans-Erik Eriksson,et al.
UML toolkit
,
1997
.
[10]
Walter Fontana,et al.
The Barrier of Objects: From Dynamical Systems to Bounded Organizations
,
1996
.
[11]
S. Kauffman,et al.
Autocatalytic replication of polymers
,
1986
.
[12]
John H. Holland,et al.
Hidden Order: How Adaptation Builds Complexity
,
1995
.