add perl example code
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{
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double n_4639, n_4920, n_601, n_1863, n_7065, n_output;
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n_4639 = 1 / (1 + exp(-1 * 5 * (
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0.0000000000000000000000000000000000000000000000000000000
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+ (n_one * -0.7865729571529459507317483257793355733156204223632812500)
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+ (n_two * 0.8809346930053569835195048653986304998397827148437500000)
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)));
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n_4920 = 1 / (1 + exp(-1 * 5 * (
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0.0000000000000000000000000000000000000000000000000000000
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+ (n_one * 0.8143148554908640202043557110300753265619277954101562500)
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+ (n_4639 * 0.7663975023791630425407106486090924590826034545898437500)
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+ (n_two * -0.2546305433438110221722183723613852635025978088378906250)
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)));
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n_601 = 1 / (1 + exp(-1 * 5 * (
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0.3050293740764479788474261567898793146014213562011718750
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+ (n_one * -1.7579643686798800406023701725644059479236602783203125000)
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+ (n_4639 * -0.7117026436492900343111500660597812384366989135742187500)
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+ (n_4920 * 0.6600543182741239744970584979455452412366867065429687500)
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+ (n_two * -1.0312303447979600523609633455635048449039459228515625000)
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)));
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n_1863 = 1 / (1 + exp(-1 * 5 * (
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0.0000000000000000000000000000000000000000000000000000000
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+ (n_one * 0.4067102473422800068547644514183048158884048461914062500)
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+ (n_4639 * 0.8935708684473929741542974625190254300832748413085937500)
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+ (n_two * -0.0171564060099979008577886219200081541202962398529052734)
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+ (n_601 * 0.5111012475277429967235320873442105948925018310546875000)
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)));
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n_7065 = 1 / (1 + exp(-1 * 5 * (
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-0.0614492704162033429038380916153982980176806449890136719
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+ (n_4639 * 0.7968332799177390013412036751105915755033493041992187500)
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+ (n_601 * 0.5417244666303689948350097438378725200891494750976562500)
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)));
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n_output = 1 / (1 + exp(-1 * 5 * (
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0.0520650055457407973102945675236696843057870864868164062
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+ (n_1863 * -3.8456383168042500564354213565820828080177307128906250000)
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+ (n_one * 10.0000000000000000000000000000000000000000000000000000000)
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+ (n_4920 * 0.1524713339586759863752973842565552331507205963134765625)
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+ (n_7065 * -0.2138580237347930124158779108256567269563674926757812500)
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+ (n_two * 10.0000000000000000000000000000000000000000000000000000000)
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+ (n_601 * -2.4282240918724999900746297498699277639389038085937500000)
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)));
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return n_output;
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}
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File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,176 @@
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Generations
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# calculate number of offspring allowed per species
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for each species,
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identify the average and the min/max fitness values
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add 1 to all of those numbers (for mathematical simplicity)
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take the sum of the averages
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for each species
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divide the total by the species average
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multiply that number by the target population size
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subtract from it that number times stagnant generations count
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store the floor()ed result as allowed offspring count
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count the total offspring allocated above
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if less than target population size
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allocate remaining to most-fit species
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for each species
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multiply offspring by 10% to get the 'elitism' offspring count
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if elitism less than 2, set it at 2
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if this is more than the total offspring allowed, species dies
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subtract the elitism count from the allowed count to get remaining
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allocate 50% to be created via crossover
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50% will include only some weights from less fit parent
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50% will include one additional structural element, too
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and the other 50% via mutation
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all of which are weight adjusted by up to 10% per weight
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65% of which are by weight adjustment only
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20% add a new connection
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10% split a current connection / add node
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5% delete an existing connection
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# identify rankings within species
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for each species,
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make a list of members sorted by fitness, descending
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# identify dominance within species
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# used to determine breeding priority
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for each species
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calculate sum of fitness values of all members of species
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for each member of species
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calculate fitness / sum of fitness
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# elitism
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take the first N networks from the sorted species member list
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copy each one, verbatim, to the next generation
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# mutation
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for each species
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calculate 'remaining' as number of mutation offspring allowed
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for each member of the species, in fitness order (descending)
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multiply its dominance value times allowed offspring
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round() the number to the nearest integer.
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if number is 0, round up to 1
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if number is greater than remaining, reduce to remaining
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while this number is > 0
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create a new mutation of this organism
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and copy it to the next generation
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decrement the number, and the 'remaining' number
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if 'remaining' is 0, exit loop
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# to mutate a single organism
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copy the organism to a new structure
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give the new one a new ID
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for each connection
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assign a new weight that is +/- up to 10% of current value
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save random() number
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if random < delete threshold, delete a connection
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else if < split threshold, split a connection
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else if < add threshold, add a connection
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# crossover
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for each species
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calculate 'remaining' as number of offspring allowed, times 2
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for each member of the species, in fitness order (descending)
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multiply its dominance value times allowed offspring
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round() the number to the nearest integer.
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if number is 0, round up to 1
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if number is greater than remaining, reduce to remaining
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push this member onto a list of breeders <number> times
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randomize the list of breeders
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while the list of breeders is not empty
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pop the first entry off the front of the list
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search the list for the first entry that is not the same
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while the first entry is the same as the other entry
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pop it off and push it on to the end
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crossover these two to make a new one
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# to crossover two specimens
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copy the more fit parent's genome
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for each connection in the more fit parent's genome
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if a match occurs in less fit parent
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50/50 chance of using the weight from the less fit parent
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50/50 chance of selecting ONE structural element from the less
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fit parent and copying it into the new genome
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move the newly created offspring into the next generation
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# speciation
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-initially, each member belongs to the species of its parents.
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-if two organisms have the same structure, they belong in the same
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species, regardless of who their parents were.
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for every member of the population
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compose a string representing the structure of the network
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store a hash entry with structure as key
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store in the record
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list of members
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highest fitness value of any member
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total fitness of all members
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count of members
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for each structure
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calculate average fitness
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sort the structures by highest average fitness
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start with an empty species list
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for each structure, while species count < max
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form a new species with that structure as basis
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sort the members by fitness, highest first
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for the rest of the structures
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identify the structure(s) that are most compatible with this one
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if only one, add all members to that species
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otherwise, assign each member to the compatible structure whose
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average is closest to that of the new member
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and update the average for that species
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for each new species
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build a list of records with:
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new species (pointer),
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old species name or proposed new name,
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number of members from that old species in this new species
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average fitness of those members
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average fitness of new species
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sort that master list by
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average fitness of members voting for a particular assignment
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average fitness of new species
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number of members voting for name
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go through the list of species name votes
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if the proposed name is already taken, reject the vote
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if the proposed species has already been given a name, reject
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otherwise, assign that name to that species
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go through the list of species, in fitness order, descending
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if the species doesn't have a name, give it a new one
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give every member the name of their new species, if different
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offspring priority per topology
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1st place network copied verbatim - elite
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1st elite with randomly 'adjusted' weights
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1st elite with 'average' crossover with 2nd place
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1st elite with 'random' crossover with 2nd place
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2nd place network copied verbatim
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2nd elite with randomly adjusted weights
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1st elite with 'average' crossover with random other
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2nd elite with 'average' crossover with random other
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1st elite with 'random' crossover with random other
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2nd elite with 'random' crossover with random other
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all new weights
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if after allocating offspring, there remain open slots
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one of
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75% - add a new connection
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20% - split a node
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5% - delete a connection
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as topologies optimize, their improvement slows
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when it ceases to improve, the investment in it decreases
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stagnation penalty incremented
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when an improvement is made, cut the penalty in half
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when the investment drops below threshold, the elite is 'archived'
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and further investment in it ceases
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if a mutation produces this same topology again,
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treat the event as an improvement
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cut stagnation penalty in half
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include archived elite and new member as gene pool
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as the investment in a topology decreases,
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the more resources are available for more promising avenues
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instead of 'species' we will call it 'topology'
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@@ -0,0 +1,68 @@
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$data = {
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# settings controlling the operation of the experiment
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'settings' => {
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'experiment' => 'xor',
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'population' => 100, # desired number of networks
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'limit' => 10, # max offspring per topology
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'penalty' => 10, # max investment/stagnant topology
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'retest-elites' => 0,
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'save-epochs' => 0,
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'activation' => 'perl',
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'max-weight' => 10,
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'gain' => 5,
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'debug' => 0,
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'topology-limit' => 50,
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# 'allow-recurrent' => 1,
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},
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# the state of the experiment
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'save' => {
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'unique-id' => 0, # innovation id
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'generation' => 0, # which generation this is
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'max-fitness' => 0,
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'max-weight' => 0,
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'min-weight' => 0,
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},
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# nodes listed by the unique node-id
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'nodes' => {
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'one' => {
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'id' => 'one', # redundant, but useful
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'type' => 'input',
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},
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'two' => {
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'id' => 'two', # redundant, but useful
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'type' => 'input',
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},
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'bias' => {
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'id' => 'bias', # redundant, but useful
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'type' => 'input',
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},
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'output' => {
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'id' => 'output', # redundant, but useful
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'type' => 'output',
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'function' => 'sigmoid',
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'gain' => '5',
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},
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},
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# one entry per network in the population
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'population' => {
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'initial' => {
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'id' => 'initial',
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'fitness' => 0,
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'elite' => 0,
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'nodes' => {
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'one' => {},
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'two' => {},
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'bias' => {},
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'output' => {},
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},
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},
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},
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# a record of all the topologies in the population
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# this tracks statistics about each one
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'topologies' => {},
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# for all nodes formed by splitting connections, the
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# mapping between the nodes connected and the node created
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# exists primarily for search-speed
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# can be removed before saving,
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'node-lookup' => {},
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};
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File diff suppressed because it is too large.
Load diff
Executable
+71
@@ -0,0 +1,71 @@
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#!/usr/bin/perl -w
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use strict;
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use neat;
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my $neat = new neat( "start.xor.pl" );
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my $FINISHED = 0;
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$SIG{INT} = sub { $FINISHED = 1 };
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my $inputs = [
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{ 'bias' => 1, 'one' => 0, 'two' => 0, 'output' => 0, 'expected' => 0 },
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{ 'bias' => 1, 'one' => 1, 'two' => 0, 'output' => 0, 'expected' => 1 },
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{ 'bias' => 1, 'one' => 0, 'two' => 1, 'output' => 0, 'expected' => 1 },
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{ 'bias' => 1, 'one' => 1, 'two' => 1, 'output' => 0, 'expected' => 0 },
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];
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sub EvaluateXOR
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{
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my $net = shift;
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# why retest someone who hasn't changed?
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return $net->{'fitness'}
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if ( $net->{'elite'} );
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my $total = 0;
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my $values;
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my $average;
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my $i;
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my $difference;
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$total = 0;
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for ( $i = 0 ; $i < 4 ; $i++ )
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{
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$values = Copy( $inputs->[$i] );
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$neat->Activate( $net, $values );
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# printf( " %d%d=%lf", $values->{'one'}, $values->{'two'},
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# $values->{'output'} );
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$difference = abs( $values->{'output'} - $values->{'expected'} );
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$total += $difference;
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}
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# printf( " [1m%lf[0m\n", 1 - $average );
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my $fitness = 1 - ( $total / 4 );
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$FINISHED = 1 if ( $total < 0.04 ); # close enough!
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return $fitness;
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}
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$neat->EvaluationFunction( \&EvaluateXOR );
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# initial generation
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$neat->EvaluateAll();
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my $count = 0;
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while ( ! $FINISHED )
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{
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$neat->Epoch();
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printf( "%s\n", $neat->Stats() );
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$neat->EvaluateAll();
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#$FINISHED = 1 if ( ++$count == 100 );
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}
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$neat->Champions();
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$neat->Save( "xor.final.pl" );
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my $champion = $neat->ChampionNetwork();
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my $code = $neat->ActivationFunction_C_PartialEval( $champion,
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{ bias => 1 } );
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open INDENT, "|indent -st > activate.xor.c";
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print INDENT $code;
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close INDENT;
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#system "cat activate.xor.c";
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