In this lecture, we review the core principles of Genetic Programming, starting with Linear Genetic Programming (LGP) and transitioning to tree-based Genetic Programming (GP) that incorporates Abstract Syntax Trees (AST's) as its genotypes. We cover the different mutation operators and selection operators for these forms of GP and typical application spaces that use GP. We then close the lecture with an introduction to Immunocomputing and Artificial Immune Systems (AIS), which mimic the acquired/adaptive/specific immune system of (jawless) vertebrates. We will continue our discussion of immunocomputing/AIS in the next lecture and use it to pivot to multi-objective optimization (the subject of the next unit).

Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/men7ns3f44783gqk20b64/IEE598-Lecture2C-2026-02-17-Genetic_Programming_and_Artificial_Immune_Systems-Notes.pdf?rlkey=yyjbn4sm6wssd8no5qvnqs8ft&dl=0

Lecture 2B (2026-02-12): Evolutionary and Linear Genetic Programming

In this lecture, we start by reviewing the strengths and weaknesses of the GA, CMA-ES (with adaptive restarts), and Stochastic Gradient Descent (SGD).  This paints a picture of complementarity and not competition. Each algorithm fits within its own niche, and the algorithms can be used together to help compensate for weaknesses and find better solutions more efficiently. Whereas CMA-ES and the SGD require continuous-valued decision spaces, the GA does not, and so we then pivot to thinking about how a GA might be able to be used to write software (where code comes from a discrete decision space off limits from CMA-ES and SGD). We start this exploration with an introduction to the Evolutionary Programming of the 1960's -- which focuses on the evolution of populations of Finite State Machines (FSM's) using discrete mutation and no crossover We then think about how GA's with crossover might be able to be applied to lines of code. We start with Linear Genetic Programming (LGP), which restricts the programming language to one without multi-line control/logic blocks (where assembly languages fit within this class). We demonstrate how One-Instruction Set Computers (like Subtract and Branch if Negative, SBN) are well suited for Linear Genetic Programming (with both mutation and crossover), and we talk about how the presence of "introns" can speed up convergence in LGP (with possible implications for understanding the presence of introns in biological systems/DNA). In the next lecture, we will complete the story with Genetic Programming based on abstract syntax trees (AST's) and then introduce Artificial Immune Systems.

Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/qsv6necfvgkny8rrxqvuw/IEE598-Lecture2B-2026-02-12-Evolutionary_and_Linear_Genetic_Programming-Notes.pdf?rlkey=aux54wr6rnju9o4nlsvcptft0&dl=0

Lecture 2A (2026-02-10): Evolution Strategies and Covariance Adaptation (ES, NES, CMA-ES)

In this lecture, we introduce a fundamentally different family of evolution-inspired search algorithms, the Evolution Strategies (ES). Rather than treating a population as a set of hypothetical good solutions that must be retained or discarded, as in the GA, the Evolution Strategies adapt the search process itself by allowing different decision variables to be able to mutate using different step sizes, and the resulting adaptive step sizes reflect the curvature of the underlying fitness landscape. We discuss how this heuristic idea was formalized in Natural Evolution Strategies (NES), which leverage the information-theoretic natural gradient to learn productive directions to climb, and then how that was made more practical and effective via Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We close with a discussion of how CMA-ES facilitates adaptive restarts, making CMA-ES not only a good tool for high-resolution search of a single fitness peak but also a candidate for global optimization – seeking out new peaks in a sort of "depth-first" order (in contrast to the "breadth-first" order of the GA). We then put the GA, ES, and conventional (stochastic) gradient descent together as complementary tools for complex optimization.

Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/tnq6ol9soph5cxjcqnf73/IEE598-Lecture2A-2026-02-10-Introduction_to_Evolution_Strategies_and_CMA-ES-Notes.pdf?rlkey=9brna7e54fkh9ljf00uexxmjk&dl=0

Lecture 1H (2026-02-05): Genetic Algorithm (GA) Hyperparameter Tuning

In this lecture, we complete our coverage of the Genetic Algorithm (GA) by discussing how to improve the function of selection operators and, in general, how to tune hyperparameters to improve the performance of the GA for a given problem. We start with a discussion of the effects of Stochastic Uniform Sampling (SUS) over roulette-wheel selection and how the effective drop in variance in number of parents eliminates the fixation-causing effects of drift while also continuing to leave a barrier on precision in place. We also discuss how to use exponential ranking in ranking selection to have better control over selective pressure, but we mention that tournament selection ultimately is a stronger choice computationally when rank-based selection is desired. We discuss a framework that puts the 5 major hyperparameters (M, R, E, Pm, and Pc [as well as selection pressure]) on one graph to help guide choice of different hyperparameters based on context. We draw connections between the two types of selection operator (fitness-proportionate and rank based) and Generalized Linear Modeling (GLM; continuous and ordinal response variables) and discuss connections between the number of parents and the number of samples/statistical power in a GLM. Finally, we close with a brief introduction to Evolution Strategies (ES), which will be the topic we will start with in the next unit.

Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/8z1we6jycmealo2ww2ik0/IEE598-Lecture1H-2026-02-05-GA_Hyperparameter_Tuning-Notes.pdf?rlkey=gosm6672x8v9c66zdf6ho09mb&dl=0

Lecture 1G (2026-02-03): GA Wrap Up – Crossover, Mutation, & Tuning GA Operator Choices

In this lecture, we almost finish our discussion of the canonical Genetic Algorithm (GA) by covering different crossover and mutation operator choices. We discuss how mutation and crossover rates might change over time. We then end by returning to the selection operator to introduce Stochastic Uniform Sampling, a stratified sampling approach that reduce the variance in the number of offspring selected per high-fitness individual without affecting the mean. Next time, we will discuss how the five major hyperparameters and selection pressure work together to determine the effectiveness of the GA for a particular objective. We will also transition to Unit 2, where we will start by introducing ES and CMA-ES.

Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/rvv4bkrsiz4ixrhgitt39/IEE598-Lecture1G-2026-02-03-GA_Wrap_Up-Crossover_Mutation_and_Tuning_GA_Operator_Choices-Notes.pdf?rlkey=vcgleumzuv85moizkqibdnjhw&dl=0

Lecture 1F (2026-01-29): Operators of the Genetic Algorithm

In this lecture, we dive deeper into the basic Genetic Algorithm by describing the three major operators in any GA iteration – the selection operator, the crossover operator, and the mutation operator. We describe different forms of selection (fitness proportionate, ranking, and tournament) and how they vary in their ability to control selection pressure. We also discuss several forms of crossover (from single point to multi-point to uniform to taking random linear combinations) and their function as they move individuals around fitness landscapes. We will finish with the mutation operator next time, but that content is also covered in the pre-written slide notes linked below. After discussing the mutation operator and some optimizations of the GA itself, we will transition next to to evolutionary computing/programming.

Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/sa96xjlv5d8q3mc8l0bde/IEE598-Lecture1F-2026-01-29-Operators_of_the_Genetic_Algorithm-Notes.pdf?rlkey=54eow2a79g1437r7g19be7gjy&dl=0

Lecture 1E (2026-01-27): Structure of the Basic Genetic Algorithm

In this lecture, we reveal the basic architecture of the simple GA. We start with defining how to concretely implement chromosomes/genomes, genes, alleles, characters, and traits numerically within an Engineering Design Optimization context. We then move on to a general definition of multi-objective fitness (which we will return to in Unit 3 when we study multi-objective evolutionary algorithms) and show how fitness functions can be scaled not only to meet the assumptions on fitness functions but also to adjust selective pressure as desired. We close with a flowchart of the steps of a basic genetic algorithm, highlighting operators (selection, crossover, and mutation) that we will discuss in detail in the next lecture.

Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/zdvfjtp88fl8omly7sd6u/IEE598-Lecture1E-2026-01-27-Structure_of_the_Basic_Genetic_Algorithm-Notes.pdf?rlkey=f0t4apfkyj0v9ketqxoy1xew1&dl=0