evolutionary algorithms and artificial intelligence
DESCRIPTION
Evolutionary Algorithms and Artificial IntelligenceTRANSCRIPT
Evolutionary Algorithms and Artificial Intelligence
Paul GrouchyPhD Candidate
University of Toronto Institute for Aerospace Studies
Intro to Evolutionary Algorithms (EAs)
Program flow of a Genetic Algorithm (GA):1. Randomly initialize population of “genomes”2. Evaluate “fitness” of all genomes3. Select high-fitness genomes to become
“parents”4. Produce new population of “offspring” genomes
from “parent” genomes5. End of a single “generation”
Intro to Evolutionary Algorithms (EAs)
Toy problem: Maximize the sum of 5 bits
Genome: 0 1 1 0 0Fitness
(sum of bits)2
Intro to Evolutionary Algorithms (EAs)
Toy problem: 1 generation0 1 1 0 00 1 0 0 0 0 1 0 0 1 0 0 0 1 0
fitness: 1 fitness: 2 fitness: 2 fitness: 1
Intro to Evolutionary Algorithms (EAs)
Toy problem: 1 generation
0 1 1 0 0 0 0 0 1 0 Parents
0 1 1 1 0 0 0 0 0 0 Offspring
mutation
0 1 0 0 0
crossover point crossover point
evaluate fitness of each genome using fitness function
select and reproduce parents based on fitness values
Generation t
Generation t+1Mutation
Crossover
0 1 1 0 1
0 1 0 0 1
0 1 1 0 0 0 1 0 1 1
0 1 1 1 1
Intro to Evolutionary Algorithms (EAs)
Intro to Evolutionary Algorithms (EAs)Evolutionary Computation: A Unified Approach (2006)
Kenneth De Jong
EAs as AIs
• Eureqa (http://creativemachines.cornell.edu/eureqa)
– Based on Genetic Programming (GP):
EAs to evolve AIs
evaluate fitness of each genome using fitness function
select and reproduce parents based on fitness values
Generation t
Generation t+1
EAs to evolve AIs
evaluate fitness of each genome using fitness function
select and reproduce parents based on fitness values
0.32 1.10 -0.21 … 0.11
0 1 2
-2
-1
0
1
2
=
Learning Capabilities
Simulation environment Typical evolved forage path
Typical evolved “eat” output
THANK YOU!!!
Paul GrouchyPhD Candidate
University of Toronto Institute for Aerospace Studies