Additional Resources for Spring 2026:
Lecture Blog Posts in Chronological Order:
(unlinked lecture titles are what is tentatively planned for the future)
- 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 Genetic Algorithms: Weight/Vector-Based Approaches
- Lecture 3C (2026-02-26): Pareto Ranking Approaches
- Lecture 4A (2026-03-03): Meta-Populations in Distributed and Parallel GA's
- Lecture 4B (2026-03-05): From DGA/PGA to Niching Methods for Multi-modal Optimization
- Lecture 4C (2026-03-17): Niching Methods in Multi-Modal Optimization
- Lecture 5A (2026-03-19): Introduction to Simulated Annealing and Entropy
- Lecture 5B (2026-03-24): From Maximum Entropy (MaxEnt) Methods Toward Optimization by Simulated Annealing
- Lecture 5C (2026-03-26): Toward Simulated Annealing: Introduction to Boltzmann Sampling and Monte Carlo Integration
- Lecture 5D/6A (2026-03-31): Simulated Annealing Wrap-up and Distributed AI and Swarm Intelligence, Part 1 – Introduction to Ant Colony Optimization (ACO)
- Lecture 6B (2026-04-02): Distributed AI and Swarm Intelligence, Part 2 – Ant Colony Optimization (ACO) and Introduction to Bacterial Foraging Optimization (BFO)
- Lecture 6C (2026-04-07): Distributed AI and Swarm Intelligence, Part 3 – Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO)
- 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): Reinforcement Learning – Active Learning in Rewarding Environments
- Lecture 7E (2026-04-23): Learning without a Teacher – Unsupervised and Self-Supervised Learning
- Lecture 7F (2026-04-28): Spiking Neural Networks and Neuromorphic Computing
- Lecture 8A+ (2026-04-30): Complex Systems Models of Computation – Cellular Automata and Neighbors
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