1. Introduction Genetic programming falls into the category of evolutionary algorithms. Genetic algorithms vs. genetic programming. Concept developed by John Koza. Types of research Determining which problem domains genetic programming can successfully be applied to and compare GP with other AI techniques. Applying genetic programming to derive solutions to problems not previously solved.

2. Genetic Programming Algorithm Create the initial population Repeat Evaluate the population Select parents for the next generation Apply genetic operators Until the termination criteria are met

3. Symbolic Regression Problem Given x:11, 4, 1, 8, 5, 9, 3, 7, 2, 6 y:1742400.0, 3600.0, 0.0, 254016.0, 14400.0, 518400.0, 576.0, 112896.0, 36.0, 44100.0 What function does y represent? What else do we know? x forms part of the function One or more of +, -, /, *, sqrt forms part of the function Fitness Cases Terminal set Function Set

4. The Artificial Ant Problem What are the moves that must be made by the ant to pick up all the food pellets? (Note: the shaded squares represent food pellets) Legal Moves: Move left Move right Move up Move down Pick up food Terminal Set

5. Terminals and Functions The terminal set contains a variable representing each input to a problem. The terminal set may also include constants and operators that do not take any arguments. The function set consists of operators or legal moves, e.g. +, if, for, move-left. Terminals and functions are collectively referred to as primitives. Closure and sufficiency. Extraneous primitives degrade system performance.

6. Representing an Individual Parse trees are commonly used to represent each member of the population. Each tree is constructed by randomly choosing elements from the function and terminal sets F = { +, -, *, / } T = { x, y } F = { +, -, *, / } T = { x, y } A maximum tree depth must be specified as a GP parameter