GPU-Accelerated Parallel Gene-pool Optimal Mixing in a Gray-Box Optimization Setting

TL;DR

This paper introduces a GPU-accelerated parallel implementation of Gene-pool Optimal Mixing in Gray-Box Optimization, leveraging graph coloring to enable efficient parallel variation, significantly speeding up solutions for large, sparse Max-Cut problems.

Contribution

It presents a novel CUDA-based parallel GOM approach using graph coloring to handle dependencies, improving optimization speed in GBO settings.

Findings

Achieved up to 100x speed-up on large, sparse Max-Cut graphs.

Demonstrated effective parallelization of GOM without violating variable dependencies.

Showcased the potential of GPU acceleration in Gray-Box Optimization.

Abstract

In a Gray-Box Optimization (GBO) setting that allows for partial evaluations, the fitness of an individual can be updated efficiently after a subset of its variables has been modified. This enables more efficient evolutionary optimization with the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) due to its key strength: Gene-pool Optimal Mixing (GOM). For each solution, GOM performs variation for many (small) sets of variables. To improve efficiency even further, parallel computing can be leveraged. For EAs, typically, this comprises population-wise parallelization. However, unless population sizes are large, this offers limited gains. For large GBO problems, parallelizing GOM-based variation holds greater speed-up potential, regardless of population size. However, this potential cannot be directly exploited because of dependencies between variables. We show how graph coloring can be used to group sets of variables that can undergo variation in parallel without violating dependencies. We test the performance of a CUDA implementation of parallel GOM on a Graphics Processing Unit (GPU) for the Max-Cut problem, a well-known problem for which the dependency structure can be controlled. We find that, for sufficiently large graphs with limited connectivity, finding high-quality solutions can be achieved up to 100 times faster, showcasing the great potential of our approach.