António Manso Luís Correia
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1 António Manso Luís Correia
2 Populations, Multisets and MuGA SMuGA- Symbiogenetic MuGA Hosts and parasites Diversity guided parasite evolution Experimental results Conclusions
3 GA uses collections of individuals of individuals that evolve together Good individuals gain clones Clones decreases genetic diversity Change population representation Replace collection with multiset. Individual Fitness Population of OnesMax
4 Simple Population Individual Fitness MultiIndividual Fitness < 3, > < 2, > < 2, > < 1, > Mutiset Population Fixed dimension of MultiPopulation Controls the genetic diversity at genotype level Number of copies May be used for selection pressure
5 Start Selection MP 1 Crossover MP2 Mutation MP0 Replacement MP3 Control of the number of copies Rescaling MP4 Replacement
6 May use adapted genetic operators that explore multiset representation Multiset Selection Mutliset Crossover Multiset Mutation Multiset Replacement Adaptive Decimation Successfully applied to solve difficult problems Bitstring coded. Real coded.
7 Solving problems with large genomes
8 Species evolve together!
9 Two populations Hosts solutions of the problem Parasites partial solutions In symbiotic relationship Parasites discover good genes Parasites transfer genes to Hosts Hosts better reward parasites with good genes
10 Two phases Algorithm Evolution in isolation Survival of the fittest Collaboration Parasites infects hosts Parasites... Parasites... Parasites Evaluate Evaluate Evaluate Collaboration Collaboration Evaluate Evaluate Hosts... Hosts Hosts... Hosts Hosts
11 Tuple < position, genome> Collaboration Transference of genetic material of parasites to host Position Host * * * * * * * * * * Parasite Parasite Collaboration 0 1 * * 1 1 * * 0 1
12 Enables parasite length change a) p c) p p p Cut Point Cut Point b) o d) o o
13 Includes change of genome size Uses gene diversity information
14 Compute ocupation space of parasite population. Move parasites to less populated regions Position I I O O Parasite Population I O I I I O O O I I I I O I O O O Empty Slots Position prob. 0,21 0,16 0,11 0,11 0 0,11 0 0,05 0,11 0,16
15 Probability of break is computed by the sigmoid function Allows growth of small parasites Avoid overfit of parasites to hosts break probability 1,0 0,9 0,8 0,7 0,6 0,5 t= - 6 s= 12 0,4 0,3 t= - 6 s= 24 0,2 0,1 0,0 0,00 0,25 0,50 0,75 1,00 x = parasite size / host size
16 Compute the diversity of gene values in the parasite population Use that probability to change allele value Position I I I O Parasite Population I O I I I O O O I I I I O I O O O Prob. of bit "0" 0 0 0,5 0, , Prob. of bit "1" 0 1 0,5 0, ,
17 Heuristic Evaluation Rank individuals in host population [N 1] == [ Best... Worst ] Shift Ranks by N/ 2 [ N/2 - N/2-1] Sum ranks of the individuals that contain parasite If parasite is new: discovery reward ( > 0 ) Multiply the sum of ranks by gene diversity Beneficial and new parasites more rewarded
18 ParasiteEvolution (ppop, hpop, k) selectpop = select k parasites from ppop offspringpop = recombine selectpop while offspringpop.size < ppop.size Select random parasite from offspringpop Mutate a clone of parasite Insert mutated clone in offspringpop End while Evaluate offspringpop in hpop ppop = offspringpop End Function.
19 Functions with 8 bits Trap Intertwined Trap Trap(x) x Massively Multimodal Deceptive Problem Trap01(x) Trap 0 Trap x Concatenated functions 64, 128, 256, 512, 1024 (bits) MMDP(x) x
20 Host population size : 64 Parasite population size : 128 Iterations in isolation : 32 Evolution performed with adapted multiset genetic operators 32 independent runs
21
22
23
24 Mean number of evaluations to reach the optimum Chromossome length (bits) Trap pattern Trap01 MMDP Mean Std Mean Std Mean Std 64 5, , , , , , , , , , , , , , , , , , , , , , , , , , , , ,577, ,051.77
25 Diversity is used to guide genetic operators applied to parasites At gene level At population level (parasite position) Hosts incorporate genes discovered by parasites Efficient symbiotic model between hosts and parasites enables optimization of problems with large genomes
26
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