Journal of Chemical Information & Computer Sciences
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The Mathematica book (3rd ed.)
The Mathematica book (3rd ed.)
Evolving evolution programs: genetic programming and L-systems
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Evolving teamwork and coordination with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Artificial chemistries—a review
Artificial Life
Artificial Life Applications of a Class of P Systems: Abstract Rewriting Systems on Multisets
WMP '00 Proceedings of the Workshop on Multiset Processing: Multiset Processing, Mathematical, Computer Science, and Molecular Computing Points of View
Evolutionary Algorithms in Drug Design
Natural Computing: an international journal
Inferring a graph from path frequency
CPM'05 Proceedings of the 16th annual conference on Combinatorial Pattern Matching
Inferring a graph from path frequency
Discrete Applied Mathematics
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A simple hierarchical data structure (tree) and associated set of algorithms (written in Mathematica) have been developed that permit the direct manipulation of the topology of a molecule while simultaneously maintaining valid chemical valence. Coupled with a genetic algorithm optimization engine, these computational tools can be used to optimize chemical structures under the guidance of an appropriate fitness function. A detailed study of the factors that influence the performance of the method revealed that it is strongly dependent on the size and complexity of the evolved chemical structures. The effects of population size and choice of genetic operators are much smaller. The results of an exploration into the discovery of average molecular structures using this methodology is also described.