CAP6938 Neuroevolution and Developmental Encoding Working with NEAT Dr. Kenneth Stanley September 27, 2006

Exploiting Speciation Keep a list of species with increasing ID’s Species elitism: duplicate the species champs in the next generation –Only if species is above size 4 or 5 –Irrelevant in rtNEAT Stagnation Detection –Track generations since last improvement for each species –Drop fitnesses of stagnant species to near 0

Crossover Issue Two different connections can end up connecting the same nodes –Maybe disallow 5 9

Look Out for Bugs Gene pointing to nodes that aren’t in Gemome Gene innovation numbers out of order Node innovation numbers out of order Duplicate genes May want to check for these situations after major operations during debugging See Genome::verify() in NEAT C++ Can check whole population in a loop

Network Activation Use standard arbitrary-topology activation (Lecture 3) Do not activate in layers!

Real-time Evolution If all your agents are evaluated simultaneously, rtNEAT may be more appropriate rtNEAT produces and replaces one individual at a time (no generations) See “REAL-TIME NEUROEVOLUTION IN THE NERO VIDEO GAME ” on my homepageREAL-TIME NEUROEVOLUTION IN THE NERO VIDEO GAME Also see NERO rtNEAT source available

Your Genes May Have Extra Fields Time constants in nodes Activation function in nodes Learning parameters in connections You can have whatever you want, whatever you are evolving Should extra params affect compatibility? Remember, more fields means more possibilities, but also more dimensions

Important Methods Crossover: mate_multipoint and mate_multipoint_avg Mutation: mutate_add_connection, mutate_link_weights, mutate_enable_bits, other? Special Genomes Operations: duplicate, compare (getting the delta for compatibility distance) reproduce (inside Species) epoch (inside Population) Remember:

Developmental Encoding and Non- neural NEAT Your genes may not map directly to pieces of structure However, genes can still be marked with historical markings and complexification can still occur Need a standard starting structure

Sensors Must Be Chosen Wisely

Noisy Evaluation Randomness in an environment can cause evaluation to be inconsistent For example, in a game of chance Same individual can receive different fitness on different trials Causes confusion for evolution Not always fatal, but merits attention Maybe give all individuals in same generation same evaluation sequence

Saving and Loading Genomes/Populations should be savable and loadable Important for debugging Very disappointing to lose a good thing forever Some people use standards like XML /* Species #3 : (Size 5) (AF ) (Age 29) */ /* Organism #0 Fitness: Error: */ genomestart 0 trait trait trait node node node node node node gene gene gene gene gene gene gene gene gene genomeend 0 /* Organism #1 Fitness: Error: */ genomestart 1 trait trait trait node node node node node

Modularity Means Future Expansion (and maybe other users) Think about what you might want to do someday –Add new parameters to nodes? –Add new parameters to links? –Change the mapping? –Change platforms? –Completely different experiments? –More visualization? –Change phenotype? –Change genetyoe?

Too Much Scope = Too Much Rope Go for proof of concept What feature matters least? If nothing works what could be a prerequisite task? Incremental evolution strategies

Next Class: Developmental Encoding The power of reuse Prior work Biological underpinnings The future A Taxonomy for Artificial Embryogeny A Taxonomy for Artificial Embryogeny by Kenneth O. Stanley and Risto Miikkulainen (2003)