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): The Basic Genetic Algorithm and Its Implementation
- Lecture 1F (2026-01-29): GA Wrap Up: Options for Selection, Crossover, Mutation, and Extensions
- Lecture 2A (2026-02-03): Evolutionary Computing from Optimization to Programming (Mutation, CMA-ES, and Evolutionary Programming)
- Lecture 2B (2026-02-05): Genetic Programming on Strings and Syntax Trees
- Lecture 2C (2026-02-10): Immunocomputing: Genetic Approaches for Diverse Solution Portfolios
- Lecture 3A (2026-02-17): Multi-Criteria Decision Making, Pareto Optimality, and Introduction to Multi-Objective Evolutionary Algorithms (MOEA's)
- Lecture 3B (2026-02-19): Multi-Objective Genetic Algorithms: Weight/Vector-Based Approaches
- Lecture 3C/4A (2026-02-24): Pareto Ranking and Moving from Communities to Meta-Populations (DGA, PGA)
- Lecture 4B (2026-02-26): From DGA/PGA to Niching Methods for Multi-modal Optimization
- Lecture 4C (2026-03-03): Niching Methods in Multi-Modal Optimization
- Lecture 5A (2026-03-05): Introduction to Simulated Annealing and Entropy
- Lecture 5B (2026-03-17): From Maximum Entropy (MaxEnt) Methods Toward Optimization by Simulated Annealing
- Lecture 5C (2026-03-19): Toward Simulated Annealing: Introduction to Boltzmann Sampling and Monte Carlo Integration
- Lecture 5D/6A (2026-03-24): Simulated Annealing Wrap-up and Distributed AI and Swarm Intelligence, Part 1 – Introduction to Ant Colony Optimization (ACO)
- Lecture 6B (2026-03-26): Distributed AI and Swarm Intelligence, Part 2 – Ant Colony Optimization (ACO) and Introduction to Bacterial Foraging Optimization (BFO)
- Lecture 6C (2026-03-31): Distributed AI and Swarm Intelligence, Part 3 – Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO)
- Lecture 7A (2026-04-02): Neural Foundations of Learning
- Lecture 7B (2026-04-07): Feeding Forward from Neurons to Networks (SLP, RBFNN, MLP, and CNN)
- Lecture 7C (2026-04-09): Recurrent Networks and Temporal Supervision
- Lecture 7D (2026-04-14): Reinforcement Learning – Active Learning in Rewarding Environments
- Lecture 7E (2026-04-16): Learning without a Teacher – Unsupervised and Self-Supervised Learning
- Lecture 7F (2026-04-21): Spiking Neural Networks and Neuromorphic Computing
- Lecture 8A+ (2026-04-23): Complex Systems Models of Computation – Cellular Automata and Neighbors
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