Additional Resources for Spring 2026:
Lecture Blog Posts in Chronological Order:
- Lecture 1A (2026-01-13): Introduction to the Course, Its Policies, and Its Motivations
- Lecture 1B (2026-01-15): The Evolutionary Approach to Engineering Design Optimization
- Lecture 1C (2026-01-20): Population Genetics of Evolutionary Algorithms
- Lecture 1D (2026-01-22): The Four Forces of Evolution and the Drift Barrier
- Lecture 1E (2026-01-27): Structure of the Basic Genetic Algorithm
- Lecture 1F (2026-01-29): Operators of the Genetic Algorithm
- Lecture 1G (2026-02-03): GA Wrap Up – Crossover, Mutation, and Tuning GA Operator Choices
- Lecture 1H (2026-02-05): Genetic Algorithm (GA) Hyperparameter Tuning
- Lecture 2A (2026-02-10): Evolution Strategies and Covariance Adaptation (ES, NES, CMA-ES)
- Lecture 2B (2026-02-12): Evolutionary and Linear Genetic Programming
- Lecture 2C (2026-02-17): Genetic Programming and Artificial Immune Systems
- Lecture 2D/3A (2026-02-19): From Immunocomputing to Games and Multi-Objective Optimization (MOO)
- Lecture 3B (2026-02-24): Multi-Objective Optimality and Introduction to Multi-Objective Evolutionary Algorithms
- Lecture 3C (2026-02-26): Multi-Objective Evolutionary Algorithms From Linearization to Pareto Ranking and Beyond
- Lecture 3D/4A (2026-03-03): From Multi-Objective to Multi-Modal Optimization
- Lecture 4B (2026-03-05): Niching Methods for Diversity Preservation in Multi-Modal Optimization
- Lecture 4C (2026-03-17): From Niches to Meta-Populations: Toward Distributed and Parallel Genetic Algorithms (DGA/PGA)
- Lecture 4D/5A (2026-03-19): Distributed and Parallel GA's and Introduction to Simulated Annealing (SA)
- Lecture 5B (2026-03-24): From Entropy to Maximum Entropy (MaxEnt) Methods
- Lecture 5C (2026-03-26): Boltzmann–Gibbs and other Maximum Entropy Distributions
- Lecture 5D (2026-03-31): Metropolis–Hastings Markov Chain Monte Carlo and Simulated Annealing/Parallel Tempering
- Lecture 5E/6A (2026-04-04): Parallel Tempering and Swarm Intelligence through Social Cohesion (Particle Swarm Optimization)
- Lecture 6B (2026-04-07): Swarm Intelligence by Stigmergy: Bacterial Foraging Optimization and Ant Colony Optimization
- Lecture 7A (2026-04-09): Neural Foundations of Learning
- Lecture 7B (2026-04-14): Feeding Forward from Neurons to Networks (SLP, RBFNN, MLP, and CNN)
- Lecture 7C (2026-04-16): Recurrent Networks and Temporal Supervision
- Lecture 7D (2026-04-21): RNN's – Backpropagation Through Time (BPTT), Long Short Term Memory (LSTM), and Reservoir Computing/Echo State Networks (ESNs)
- Lecture 7E (2026-04-23): Natural Learning Experiences – Reinforcement and Unsupervised Supervised Learning
- Lecture 7F (2026-04-28): Predictive Coding, Latent Learning, and Self-Supervised Learning
- Lecture 7G (2026-04-30): Spiking Neural Networks and Neuromorphic Computing
- Lecture 8A (2026-04-30): Complex Systems Models of Computation – Cellular Automata and Neighbors
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