advanced mate selection in evolutionary algorithms
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Advanced Mate Selection in Evolutionary Algorithms. Mate Selection. Classic Mate Selection Tournament Roulette wheel Panmictic Limitations No genotypic restrictions on mating More fit individuals mate more often Fixed parameters during an EA run - PowerPoint PPT PresentationTRANSCRIPT
Advanced Mate Selection in Evolutionary Algorithms
Missouri University of Science and Technology
Mate Selection
• Classic Mate Selection– Tournament– Roulette wheel– Panmictic
• Limitations– No genotypic restrictions on mating– More fit individuals mate more often– Fixed parameters during an EA run– Time consuming process of tuning mate
selection parameters for each problem
Missouri University of Science and Technology
Mate Selection
• Mate selection with restrictions– Niching– Assortative Mating– Outbreeding
• Mate selection learning mechanisms– Reinforcement learning– LOOMS and ELOOMS
Missouri University of Science and Technology
Niching
0110
01111110
1111
1001
10000001
1110 1111 1000 0001
1111 1000
Missouri University of Science and Technology
Assortative Mating
0110
0111
1110
1111
1001
1000
0001
1111 0001
0111 1111 1000 1001
Missouri University of Science and Technology
Variable Dissortative Mating Genetic Algorithm (VDMGA)• Negative assortative mating• Hamming distance threshold
restriction– Adaptive– Restriction tends to loosen over time– Assumes dissimilarity between genotypes
improves performance• Outperforms basic assortative mating
techniques
Missouri University of Science and Technology
Outbreeding
Missouri University of Science and Technology
Reinforcement Learning in CGAs• Cellular Genetic Algorithms (CGAs)
– Individuals organized on a topological grid– More likely to mate with nearby neighbors
• Reinforcement learning based on offspring quality– Good offspring – moves individuals closer
together on the grid– Bad offspring – moves individuals further
apart on the grid
Missouri University of Science and Technology
LOOMS and ELOOMS
• Learning Offspring Optimizing Mate Selection (LOOMS)– Every individual examined all other
individuals in the population for best mate– Significant overhead
• Estimated LOOMS (ELOOMS)– Reduced overhead by looking for a good
enough mate– Features looked for in mates converged to
intermediate values
Estimated Learning Offspring Optimizing
Mate Selection(ELOOMS)
Traditional Mate Selection
25 3 8 2 4 5
MATES
5 8
5 4
• t – tournament selection• t is user-specified
ELOOMS
NOYES YES MATESYES
NOYES
YES
Mate Acceptance Chance (MAC)
(1 )
1
(1 ) ( 1)( , )
i
Lb
i ii
b dMAC j k
L
j How much do I like ?
k
b1 b2 b3 … bL
d1 d2 d3 … dL
Desired Features
j
d1 d2 d3 … dL
# times past mates’ bi = 1 was used to produce fit offspring
# times past mates’ bi was used to produce offspring
b1 b2 b3 … bL
• Build a model of desired potential mate• Update the model for each encountered mate• Similar to Estimation of Distribution Algorithms
ELOOMS vs. TGA
L=500With Mutation
L=1000With Mutation
Easy Problem
ELOOMS vs. TGA
Without Mutation With Mutation
Deceptive ProblemL=100
Why ELOOMS works on Deceptive Problem• More likely to preserve optimal
structure• 1111 0000 will equally like:
– 1111 1000– 1111 1100– 1111 1110
• But will dislike individuals not of the form:– 1111 xxxx
Why ELOOMS does not work as well on Easy Problem• High fitness – short distance to optimal• Mating with high fitness individuals –
closer to optimal offspring• Fitness – good measure of good mate• ELOOMS – approximate measure of
good mate
Learning Individual Mating Preferences
(LIMP)
Missouri University of Science and Technology
LIMP
• Individuals learn what features to look for in a mate – desired features
• Learning is based on the results of prior reproductions
• D-LIMP – each individual tracks their own desired features
• C-LIMP – desired features are tracked on a population level
Missouri University of Science and Technology
LIMP – Mate Selection
• λ individuals look for a mate• Each individual conducts a tournament
to find a mate• Comparison of desired features to
potential mates’ genes• Most suitable potential mate selected
Missouri University of Science and Technology
Mate Selection – D-LIMP
0110
1000
0111
0101
1101
1010
0001 sk
.7 | .6 | .7 | .2dj
j
sk =
.30.65
Missouri University of Science and Technology
.45
Mate Selection – C-LIMP
0110
1000
0111
0101
1101
1010
0001 sk
j
sk
.8 | .9 | .2 | .7
.3 | .4 | .8 | .8
dP0
dP1sj
=
.60
Missouri University of Science and Technology
Learning Desirable Mate Qualities• Desired features update after
recombination• Track each parent’s gene contribution
to offspring• Outcome of the reproduction is
examined– If the child is more fit than a parent, that
parent considers its mate suitable– If the child is less fit than a parent, that
parent considers its mate unsuitable
Missouri University of Science and Technology
Learning D-LIMP
0101 1010
0110
.2 | .9 | .3 | .8.7 | .6 | .7 | .2
.7 | .6 | .3 | .8
j k
mF(j)=20 F(k)=15
F(m)=18
.7 | .6 | .6 | .3 0 | 1 | .3 | .8.7 | .6 | .7 | .2 .2 | .9 | .3 | .8
Missouri University of Science and Technology
.8 | .9 | .2 | .7
.3 | .4 | .8 | .8
dP0
dP1
.8 | .9 | .1 | .7
.3 | .4 | .8 | .9
dP0
dP1 .1 | .4 | .8 | .9
.8 | 1 | .1 | .7 dP0
dP1
.8 | .9 | .2 | .7
.3 | .4 | .8 | .8
dP0
dP1
.8 | .9 | .1 | .7
.3 | .4 | .8 | .9
dP0
dP1
Learning C-LIMP
0101 1010
0110
j k
mF(j)=20 F(k)=15
F(m)=18
Missouri University of Science and Technology
Test Problems
• DTRAP– DTRAP1– DTRAP2
• NK Landscapes• MAXSAT• Performance Comparisons
– Mean Best Fitness (MBF)– Number of Evaluations until Convergence
Missouri University of Science and Technology
Tested Algorithms
• C-LIMP• D-LIMP• Variable Dissortative Mating Genetic
Algorithm (VDMGA)• Traditional Genetic Algoritm (TGA)• Survival Selection Methods
– Tournament– Restricted Tournament Replacement (RTR)
Missouri University of Science and Technology
DTRAP1 Results
Tournament RTR
Missouri University of Science and Technology
DTRAP2 vs. DTRAP1 Results
TGA
VDMGA
C-LIMP
D-LIMP
0 20 40 60 80 100
DTRAP2DTRAP1
TGA
VDMGA
C-LIMP
D-LIMP
0 20 40 60 80 100
DTRAP2DTRAP1
Tournament RTR
Missouri University of Science and Technology
NK Landscape Results
Tournament RTR
Missouri University of Science and Technology
MAXSAT Results
Tournament RTR
Missouri University of Science and Technology
DTRAP1 Convergence
Tournament RTR
Missouri University of Science and Technology
NK Landscape Convergence
Tournament RTR
Missouri University of Science and Technology
MAXSAT Convergence
Tournament RTR