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Chapter 85 : Using Genetic Algorithms To Optimize Functions of Microbial Ecosystems
Genetic algorithms (GAs) have a number of specific advantages over other optimization techniques that make them especially attractive for such use in microbial ecology. This chapter provides a general outline of the GA approach to optimization and lists a number of specific considerations for microbial ecological applications. For the microbial ecological applications discussed here in which algorithm speed is not a concern, this can be implemented by going over every gene on the chromosome of every new individual and tossing a weighted coin. From a fundamental ecological point of view, using GAs to optimize functions in microbial ecosystems offers great promise. Genetic algorithms belong to the larger field of evolutionary computation, which contains other population-based optimization methods that are similarly inspired by the biological principle of natural evolution. Evolutionary programming and evolution strategies are similar to genetic algorithms but typically do not include a recombination or crossover step. These two types of methods may provide an alternative to genetic algorithms for the optimization of functions of microbial ecosystems, especially when the optimization task is to find the appropriate level of various ecosystem factors, rather than more simply to find the right combination of such factors. The genetic programming approach essentially uses genetic algorithms to evolve computer programs, typically represented as tree structures. This approach seems less suitable for the optimization of functions of microbial ecosystems because there is no obvious way to tie an evolving computer program to the properties of an ecosystem.