Parent Selection Strategies forEvolutionary Algorithms
A Comparison of Parent SelectionStrategies Modeled After Human SocialInteraction
By Michael Ames
Motivation
Because existing research is limited,but shows significant improvementin algorithm robustness, additionalresearch is warranted.
Evolutionary algorithms modeledafter higher level human socialinteraction could produce significantimprovement in convergence timesand individual fitness.
Background
Assortative mating – mate selectionbased on the level of geneticsimilarities between mates.
Disassortative mating – mateselection based on the level ofgenetic dissimilarities betweenmates.
Background
Hamming distance - The number of bitswhich differ between two binary strings.
HD = 3
1
1
0
0
0
1
0
1
1
0
1
1
0
1
0
1
1
0
1
0
Goals
Determine difficulty ofimplementation.
Determine how well the strategieshandle premature convergence.
Measure selective pressure.
Rate performance vs. other socialstrategies
Rate performance vs. existingstrategies
Test Algorithms
N-Queens
Binary Knapsack
8-Queens
Q
Q
Q
Q
Q
Q
Q
Q
Binary Knapsack
0
0
0
1
1
1
0
0
0
0
0
1
1
0
1
0
1
0
1
0
0
1
0
1
0
0
1
1
0
1
.157
.1634
.201
.084
.0657
.13
.457
.0265
.167
.0954
.068
.024
.24
.351
.0214
.1307
.1953
.03
.0168
.2451
.0951
.124
.1548
.028
.1641
.13
.0651
.158
.2687
.1241
1
0
1
1
0
1
1
1
1
1
1
0
0
0
1
0
1
0
1
0
0
1
0
0
1
1
1
1
0
0
0
0
1
0
1
0
0
1
0
1
0
1
1
0
1
1
1
1
0
0
0
0
1
1
0
1
0
1
0
0
0
1
1
0
0
1
1
0
1
1
0
0
1
0
1
Approach
Implement the evolutionaryalgorithms
Implement a separate mateselection object.
Results NQueen
Results NQueen
Tournament – generations:3925
time:115.46 sec
AMEA Marriage – generations: 5316
time:168.43 sec
AMEA Low Cheat – generations: 4876
time:142.57 sec
AMEA High Cheat – generations: 4215
time:127.31 sec
Results Knapsack
Results Knapsack
Tournament – generations:6975
best:27.9844
AMEA Marriage – generations: 6350
best:27.7853
AMEA Low Cheat – generations: 6550
best:27.4023
AMEA High Cheat – generations: 6825
best:27.8461
Conclusions
Poor performance
Longer time / inferior results
Future Work
Develop other evolutionary matingstrategies
Implement one of the algorithmswith two separate populationsdynamic in size, one designatedmale the other female, then applythe same or similar strategies
Any of these?
