Static Segregative Genetic Algorithm for Optimizing Variable Ordering of ROBDDs

This paper presents a segregative genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The main components are a basic genetic algorithm and two feature functions used to measure the similarity between chromosomes. Many copies of the basic genetic algorithm explore in parallel subpopulations induced in the search space by clustering in the feature space. A communication protocol preserves the similarity within each subpopulation during the evolution process. An associative tabu search memory is used to avoid reexploration of the search space. Extensive experimental evaluation proves the efficiency and stability of the segregative approach, which systematically produces better results than the basic genetic algorithm. The efficiency of the distributed implementation in terms of resource usage and many aspects regarding the communication protocol between different components are thoroughly described. The experiments used classical benchmarks known as very difficult and show that the segregative variant is better than the monopopulation algorithm and the approach using the island model.