Generations

# calculate number of offspring allowed per species
for each species, 
	identify the average and the min/max fitness values
	add 1 to all of those numbers (for mathematical simplicity)
take the sum of the averages
for each species
	divide the total by the species average
	multiply that number by the target population size
	subtract from it that number times stagnant generations count
	store the floor()ed result as allowed offspring count
count the total offspring allocated above
if less than target population size
	allocate remaining to most-fit species
for each species
	multiply offspring by 10% to get the 'elitism' offspring count
	if elitism less than 2, set it at 2
	if this is more than the total offspring allowed, species dies
	subtract the elitism count from the allowed count to get remaining
	allocate 50% to be created via crossover
		50% will include only some weights from less fit parent
		50% will include one additional structural element, too
	and the other 50% via mutation
		all of which are weight adjusted by up to 10% per weight
		65% of which are by weight adjustment only
		20% add a new connection
		10% split a current connection / add node
		 5% delete an existing connection

# identify rankings within species
for each species, 
	make a list of members sorted by fitness, descending

# identify dominance within species
# used to determine breeding priority
for each species
	calculate sum of fitness values of all members of species
	for each member of species
		calculate fitness / sum of fitness

# elitism
take the first N networks from the sorted species member list
copy each one, verbatim, to the next generation

# mutation
for each species
	calculate 'remaining' as number of mutation offspring allowed
	for each member of the species, in fitness order (descending)
		multiply its dominance value times allowed offspring
		round() the number to the nearest integer.
		if number is 0, round up to 1
		if number is greater than remaining, reduce to remaining
		while this number is > 0
			create a new mutation of this organism
			and copy it to the next generation
			decrement the number, and the 'remaining' number
		if 'remaining' is 0, exit loop

	# to mutate a single organism
	copy the organism to a new structure
	give the new one a new ID
	for each connection
		assign a new weight that is +/- up to 10% of current value
	save random() number
	if random < delete threshold, delete a connection
	else if < split threshold, split a connection
	else if < add threshold, add a connection

# crossover
for each species
	calculate 'remaining' as number of offspring allowed, times 2
	for each member of the species, in fitness order (descending)
		multiply its dominance value times allowed offspring
		round() the number to the nearest integer.
		if number is 0, round up to 1
		if number is greater than remaining, reduce to remaining
		push this member onto a list of breeders <number> times
	randomize the list of breeders
	while the list of breeders is not empty
		pop the first entry off the front of the list
		search the list for the first entry that is not the same
		while the first entry is the same as the other entry
			pop it off and push it on to the end
		crossover these two to make a new one

	# to crossover two specimens
	copy the more fit parent's genome
	for each connection in the more fit parent's genome
		if a match occurs in less fit parent
			50/50 chance of using the weight from the less fit parent
	50/50 chance of selecting ONE structural element from the less
		fit parent and copying it into the new genome
	move the newly created offspring into the next generation

# speciation
-initially, each member belongs to the species of its parents.
-if two organisms have the same structure, they belong in the same 
species, regardless of who their parents were.

for every member of the population
	compose a string representing the structure of the network
	store a hash entry with structure as key
	store in the record
		list of members
		highest fitness value of any member
		total fitness of all members
		count of members
for each structure
	calculate average fitness
sort the structures by highest average fitness
start with an empty species list
for each structure, while species count < max
	form a new species with that structure as basis
	sort the members by fitness, highest first
for the rest of the structures
	identify the structure(s) that are most compatible with this one
	if only one, add all members to that species
	otherwise, assign each member to the compatible structure whose 
	  average is closest to that of the new member
	  and update the average for that species
for each new species
	build a list of records with:
	  new species (pointer),
	  old species name or proposed new name,
	  number of members from that old species in this new species
	  average fitness of those members
	  average fitness of new species
sort that master list by 
	average fitness of members voting for a particular assignment
	average fitness of new species
	number of members voting for name
go through the list of species name votes
	if the proposed name is already taken, reject the vote
	if the proposed species has already been given a name, reject
	otherwise, assign that name to that species
go through the list of species, in fitness order, descending
	if the species doesn't have a name, give it a new one
give every member the name of their new species, if different
	
	

offspring priority per topology
	1st place network copied verbatim - elite
	1st elite with randomly 'adjusted' weights
	1st elite with 'average' crossover with 2nd place
	1st elite with 'random' crossover with 2nd place
	2nd place network copied verbatim
	2nd elite with randomly adjusted weights
	1st elite with 'average' crossover with random other
	2nd elite with 'average' crossover with random other
	1st elite with 'random' crossover with random other
	2nd elite with 'random' crossover with random other
	all new weights

if after allocating offspring, there remain open slots
	one of
		75% - add a new connection
		20% - split a node
		 5% - delete a connection
	
as topologies optimize, their improvement slows
when it ceases to improve, the investment in it decreases
	stagnation penalty incremented
when an improvement is made, cut the penalty in half
when the investment drops below threshold, the elite is 'archived'
 and further investment in it ceases
if a mutation produces this same topology again,
	treat the event as an improvement
		cut stagnation penalty in half
		include archived elite and new member as gene pool

as the investment in a topology decreases, 
 the more resources are available for more promising avenues

instead of 'species' we will call it 'topology'