Genetic Programming - MOSES

MOSES

Meta-Optimizing Semantic Evolutionary Search (MOSES) is a meta-programming technique for evolving programs by iteratively optimizing genetic populations. It has been shown to strongly outperform genetic and evolutionary program learning systems, and has been successfully applied to many real-world problems, including computational biology, sentiment evaluation, and agent control. When applied to supervised classification problems, MOSES performs as well as, or better than support vector machines (SVM), while offering more insight into the structure of the data, as the resulting program demonstrates dependencies and is understandable in a way that a large vector of numbers is not.

MOSES is able to out-perform standard GP systems for two important reasons. One is that it uses estimation of distribution algorithms (EDA) to determine the Markov blanket (that is, the dependencies in a Bayesian network) between different parts of a program. This quickly rules out pointless mutations that change one part of a program without making corresponding changes in other, related parts of the program. The other is that it performs reduction to reduce programs to normal form at each iteration stage, thus making programs smaller, more compact, faster to execute, and more human readable. Besides avoiding spaghetti code, normalization removes redundancies in programs, thus allowing smaller populations of less complex programs, speeding convergence.