In this lecture, we wrap up our units on evolutionary algorithms, closing on Distributed (Island Model) and Parallel Genetic Algorithms. We describe the basic population structure and migration approaches in Distributed GA's and explore whether Sewall Wright's shifting-balance theory (SBT) can explain DGA's success on certain landscapes. We then pivot to a new unit on physics-inspired ML and optimization approaches, where Simulated Annealing (SA) is one of the key topics. We introduce Simulated Annealing and discuss how hardware annealers can solve a broad set of combinatorial problems that can be QUBO (Quadratic Unconstrained Binary Optimization) encoded. We setup the basic content grammar for the unit by introducing macrostate, microstate, temperature, and energy, and then we give an animated outline of how the basic SA algorithm works. We will use this SA to motivate our explorations into entropy, MaxEnt, Boltzmann sampling, and more in future lectures in this unit.
Shifting-Balance Theory visualizer: https://tpavlic.github.io/asu-bioinspired-ai-and-optimization/shifting_balance_theory/sbt_four_peaks.html
Simulated Annealing explorer: https://tpavlic.github.io/asu-bioinspired-ai-and-optimization/simulated_annealing/simulated_annealing_demo.html
Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/b8v78jmem4j9spju7sa8k/IEE598-Lecture4D_5A-2026-03-19-Distributed_and_Parallel_GAs_and_Introduction_to_Simulated_Annealing_SA-Notes.pdf?rlkey=qfh29uk7ckfb8aphn1k645r9e&dl=0
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