Taming the Complexity of Evolutionary Dynamics

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The study of complex adaptive systems is a crucial modern scientific endeavor, as these systems exhibit vastly different behaviors across scales and environments. Understanding their emergent behavior poses significant challenges, particularly in mathematical modeling. A new methodology is needed to analyze complex macroscopic phenomena. Coarse graining, a technique from statistical physics, addresses this by identifying a subset of collective variables that provide a compact and computationally manageable description of systems with numerous microscopic degrees of freedom. The authors introduce the fundamentals of natural and artificial evolutionary dynamics, delving into models of search spaces, population spaces, state spaces, crossover, mutation, and selection. The book further explores the mathematical modeling of these evolutionary dynamics through the coarse graining technique and analyzes the resulting models. This work represents a substantial contribution to the theory of artificial evolutionary systems, making it essential reading for theoreticians in computer science, artificial intelligence, and engineering. Additionally, its insights into managing complexity will benefit biologists and physicists studying natural evolutionary systems.