1. Dr. Kenneth Stanley September 11, 2006
CAP6938 Neuroevolution and Developmental Encoding Evolutionary Comptation
Dr. Kenneth Stanley
September 11, 2006

2. Main Idea Natural selection can work on computers
Selection: Picking the best parents
Variation: Mutation and Mating
Start with some really bad individuals
Some are always better than others
Survival of the fittest leads to improvement
Progress occurs over generations

3. Survival of the Roundest
Gen 1
Select as parents
Gen 2
Select as parents
Gen 3
Champ!

4. Several Versions of EC Genetic Algorithms (Holland 1960s)
Evolution Strategies (Rechenberg 1965)
Evolution Programming (Fogel 1966)
Genetic Programming? (Smith 1980,Koza 1982)
The process is more important than the name

5. Major Concepts Genotype and Phenotype Representation / mapping
Evaluation and fitness
Generations
Steady state
Selection
Mutation
Mating/Crossover/Recombination
Premature Convergence
Speciation

6. Genotype and Phenotype
Genotype means the code (e.g. DNA) used to the describe an organism, i.e. the “blueprint”
Phenotype is the organism’s actual realization

7. Representation and Mapping
The genotype is a representation of the phenotype; how to represent information is a profound and deep issue
The process of creating the phenotype from the genotype is called the genotype to phenotype mapping
Mapping can happen in many ways

8. Mappings

9. Evaluation and Fitness
The phenotype is evaluated, not the genotype
The performance of the phenotype during evaluation is its fitness
Fitness tells us which genotypes are better than others

10. Generations Most GAs proceed in generations:
A whole population is evaluated one at a time
That is the current generation
They then are replaced en masse by their offspring
The replacements form the next generation
And so on…

11. Steady State Evolution
Not all EC is generational
It is possible to replace only one individual at a time, i.e. steady state evolution
Common in Evolution Strategies (ES)
Also called real-time or online evolution
Another twist: Phenotypes can be evaluated simultaneously and asynchronously

12. Selection
Selection means deciding who should be a parent and who should not
Selection is usually based on fitness
Methods of selection (see Mitchell p.166)
Roulette Wheel (probability based on fitness)
Truncation (random among top n%)
Rank selection (use rank instead of fitness)
Elitism (champs get to have clones)

13. Mutation Mutation means changing the genotype randomly
Can vary from strong (every gene mutates) to weak (only one gene mutates)
May mean adding a new gene entirely
Mutation prevents fixation
Mutation is a source of diversity and discovery

14. Mating Combining one or more genomes Many ways to implement crossover:
Singlepoint
Multipoint (Uniform)
Multipoint average (Linear)
How important is crossover?
What is it for?

15. Premature Convergence
When a single genotype dominates the population, it is converged
Convergence is premature if a suitable solution has not yet been found
Premature convergence is a significant concern in EC
Hence the need to maintain diversity

16. Speciation A population can be divided into species
Can prevent incompatibles from mating
Can protect innovative concepts in niches
Maintains diversity
Many methods
Islands
Fitness sharing
Crowding

17. Natural Evolution is not Just Optimization
What is the optimum?
What is the space being searched?
What are the dimensions?
Herb Simon (1958): “Satisficing”
Is evolution even just a satisficer?
Evolution satisfices and complexifies

18. Next Class: Theoretical Issues in EC
The Schema Theorem
No Free Lunch
Homework:
Mitchell pp , and ch.5 (pp )
No Free Lunch Theorems for Optimization
by Wolpert and Macready (1996)