Revolutionary Algorithms

TL;DR

Revolutionary algorithms combine evolutionary, cultural, and political concepts to improve the efficiency of dynamic problem optimization, addressing limitations of existing methods like parallel genetic and cultural algorithms.

Contribution

The paper introduces revolutionary algorithms that integrate political dynamics into evolutionary and cultural frameworks for more efficient dynamic optimization.

Findings

Revolutionary algorithms adapt more quickly to changing environments.

They outperform traditional genetic and cultural algorithms in dynamic settings.

The approach offers a novel way to model belief system changes through revolution.

Abstract

The optimization of dynamic problems is both widespread and difficult. When conducting dynamic optimization, a balance between reinitialization and computational expense has to be found. There are multiple approaches to this. In parallel genetic algorithms, multiple sub-populations concurrently try to optimize a potentially dynamic problem. But as the number of sub-population increases, their efficiency decreases. Cultural algorithms provide a framework that has the potential to make optimizations more efficient. But they adapt slowly to changing environments. We thus suggest a confluence of these approaches: revolutionary algorithms. These algorithms seek to extend the evolutionary and cultural aspects of the former to approaches with a notion of the political. By modeling how belief systems are changed by means of revolution, these algorithms provide a framework to model and optimize dynamic problems in an efficient fashion.