Web service composition can be thought of as the combination of reusable functionality modules available over the network to create applications that accomplish more complex tasks. Evolutionary Computation (EC) techniques have been applied with success to the problem of fully automated Web service composition, which is when candidate services are selected at the same time that the best configuration in which to connect those candidates is determined. Genetic Programming (GP) is the EC technique traditionally employed in fully automated Web service composition, with solutions encoded as trees instead of their natural Directed Acyclic Graph (DAG) form. The problem with a tree representation is that it complicates the enforcement of dependencies between service nodes, which is much easier accomplish in a DAG. This motivates the proposal of GraphEvol, an evolutionary technique that uses DAGs directly to represent and evolve Web service composition solutions. GraphEvol is analogous to GP, but it implements the mutation and crossover operators differently. Experiments were carried out comparing GraphEvol with GP for a series of composition tasks, with results showing that GraphEvol solutions either match or surpass the quality of those obtained using GP, at the same time relying on a more intuitive representation.
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