Genetic Algorithms in Computer-Aided Molecular Design

Publisher Summary Genetic algorithms, which are based on the principles of Darwinian evolution, are widely used for combinatorial optimizations. This chapter introduces the art and science of genetic algorithms and reviews different applications in computer-aided molecular design. A genetic algorithm operates through a simple cycle of stages, including encoding mechanism, creation of a population of chromosomes, definition of a fitness function, and genetic manipulation of the chromosomes. Genetic algorithms are successfully used in different steps required for deriving, analyzing, and validating quantitative structure–activity relationship (QSAR) models. A large number of software packages, which propose genetic algorithms and/or related methods are available commercially or in the public domain, including Distributed Parallel Genetic Algorithm (DGPA), AGAC, EvoFrame, Evolver, GAGA, GAME, OMEGA Predictive Modeling System, and GENESIS. Genetic algorithms are robust, adaptive search methods that can be immediately tailored to real problems. In addition, genetic algorithms are very easy to parallelize for exploiting the capabilities of massively parallel computers and distributed systems. Despite the successful use of genetic algorithms to solve various optimization problems, progress with regard to research on their theoretical foundations is needed.

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