Breaking the Cycle: Exploring the Advantages of Novel Evolutionary Cycles

There have been many different forms of evolution-ary algorithms (EAs) designed by humans over the past 50 years, with many variants optimized for specific classes of problems. As computational resources grow, the automated design of EAs has become an increasingly viable method for improving performance. However, many components of EAs have been treated as largely immutable, for both human and automated designers, dramatically constraining design space. In particular, the evolutionary cycle (the repeating pattern of reproduction and survival) has little or no differences between most popular forms of EA. In a previous paper, we proposed a technique for automatically designing evolutionary cycles using directed graphs, greatly increasing the explorable design space. In this paper, we showcase an improved representation and evolutionary process, provide preliminary experiments demonstrating that EAs produced by this process can outperform those with a traditional cycle, and explore the phenotype landscape to show that the new space explored by our technique may contain better EAs than traditional cycles allow.