# CS 472/572

### CS 472/572: Evolutionary Computation

**Catalog Description:** Solving computation problems by "growing" solutions; simulates natural evolution using analogies of mutation, crossover, and other genetic transformations on representations of potential solutions; standard EC techniques such as genetic algorithms and evolutionary programming, mathematical explanation of why they work, and a survey of some applications; the focus is on solving real-world problems using projects.

**Type:** CS 472 is a Technical Elective for CS majors. CS 572 is available for graduate credit.

**Total Credits:** 3

**Course Coordinator**: Terry Soule

**URL:** http://marvin.cs.uidaho.edu/Teaching/CS472/index.html

**Prereq:** CS 210

**Textbook:** Eiben and Smith, *Introduction to Evolutionary Computing*, Springer, 2010, or equivalent text.

**Prerequisites by Topic:**

- Basic graph theory
- Object-oriented programming skills
- Recursion
- Arrays and trees
- Search algorithms
- Parse trees and expression trees

## Major Topics Covered

- Search and optimization (5 hours) (IS2)
- Genetic algorithms (5 hours)
- Evolutionary strategies (3 hours)
- Genetic programming and evaluation trees (3 hours) (PL8)
- Algorithms optimization – parameter choice (3 hours)
- Evolutionary algorithm theory (3 hours)
- Performance measures (3 hours)
- Particle swarm optimization and hybrids algorithms (3 hours)
- Constraint problems (3 hours) (IS2)
- Agent teams (2 hours) (IS6)
- Co-evolution (3 hours)

## Course Outcomes

After taking CS 472/572, a student should:

- Be able to take a complex problem and design an appropriate evolutionary algorithm to solve it (or recognize why an evolutionary algorithm is not an appropriate solution). Including: (c)
- Picking or designing an appropriate solution representation
- Picking or designing an appropriate fitness function
- Picking and designing an appropriate evolutionary algorithm
- Picking or designing appropriate mutation, crossover, and selection operations

- Be able to write algorithms for: (k)
- Local search (e.g. simulated annealing)
- A genetic algorithm
- A genetic program

- Understand: (k)
- Fitness functions
- Search landscapes
- Epistasis
- The concept of search neighborhoods
- Problem representations