Efficient genetic programming based on binary decision diagrams

The performance of genetic programming can be dramatically improved by using a data structure coded by binary decision diagrams (BDDs). BDDs are a compact representation of Boolean functions using directed acyclic graphs. Efficient BDD-based crossover, mutation, and evaluation algorithms have been developed that allow all genetic operations to be performed on BDDs throughout the search. BDD-based GP reduces storage requirements by sharing isomorphic sub-graphs among individuals, and saves computational power by using a hash-based cache to make calculation more efficient. The proposed approach is powerful enough to solve the 20-multiplexer problem, which has never been reportedly achieved before