1. Evolutionary Algorithms Jim Whitehead
UC Santa Cruz
School of Engineering
courses.soe.ucsc.edu/courses/cmps265/Spring14/01
6 May 2014

2. Evolutionary Algorithms
An approach for searching large solution spaces
As a generative method, a way of finding acceptable content in a large space of possible content
Inspired by biological evolution
Has populations of solutions that are selected via a process of reproduction, mutation, recombination, and selection
Good for finding solutions to many kinds of problems
Evolutionary approach can be applied in many contexts

3. Progress of Evolutionary Algorithm
Have a fixed population of “individuals” (candidate solutions)
Evaluation is with respect to a “fitness function”
Usually related to one or more desired qualities of the solution
Also: “objective function”
New population is created by selecting some of the most fit (parents) and then combining them (reproduction), and adding randomness (mutation)

4. Fitness Function Captures the qualities of desirable solutions
Often function that outputs a real number representing the goodness of a particular solution.
Can also be a vector of real numbers
Makes it possible to determine one solution is better than another
For generative methods, developing a fitness function can be difficult
What is a good fitness function for a dungeon? A platformer level?
Sometimes you have humans act as the fitness function…
Ideally should be inexpensive to compute
Will be evaluated many times
Leads to fitness functions that approximate desirable solutions: once strong solutions are found, can perform more expensive evaluation

5. Selection Picking individuals to contribute to the next generation
Roulette wheel (fitness proportional)
Individuals evaluated for fitness. Probability of selection increases with fitness.
Most fit tend to be selected, but some lesser fit individuals will also be selected
Tournaments
Run several tournaments among individuals selected at random
Winner of each tournament is selected for crossover (or, has increased probability of selection for crossover)

6. Crossover (Reproduction)
How to vary individual solutions from one generation (iteration) to the next
Performed on selected individuals, so, generally, individuals with higher fitness
In general, idea is to exchange information between parents
Many approaches, depends on representation of individuals
Assuming individuals represented as strings:
One-point crossover
Two-point crossover
Uniform crossover

7. Mutations
Allows for insertion of randomness into solution search process
Avoids getting stuck in a local optima
Typically some number of individuals are selected for mutation
Can be performed on individuals in lieu of crossover, or after crossover
Many possibilities
For bit string: flip bits at random positions
For ranges: randomly select value in valid range (either with uniform, or Gaussian distribution)

8. Representing individuals
Major challenge: how to map solutions into a representation suitable for genetic evolution
Major dividing line between evolutionary approaches
Strings of numbers (often binary): genetic algorithms
Trees (representing programs): genetic programming
Vectors of real numbers: evolution strategy
Artificial neural networks (ANNs): neuroevolution

9. Generative methods
Search-based procedural content generation: Evolutionary approaches for generating content for games
“Search-based” term used so as to also include other approaches such as simulated annealing, particle swarm optimization, random local search
Different PCG approaches (from Search-Based Procedural Content Generation: A Taxonomy and Survey, Togelius et al.)