1. INVESTIGATING REPLACEMENT STRATEGIES
FOR THE
ADAPTIVE DISSORTATIVE MATING GENETIC
ALGORITHM
Carlos Fernandes1,2
J.J. Merelo1
Agostinho C. Rosa2
1
Department of Architecture and Computer Technology, University of Granada, Spain
2
L aSEEB-ISR-IST, Technical Univ. of Lisbon (IST), Portugal

2. SUMMARY
ADMGA
Non-Stationary Fitness Landscapes
Motivation
Replacement Strategies
Results
Conclusions and Future Work

3. Dissortative MatingDissortative Mating
 Mating between dissimilar individuals.
 Higher diversity.
Disruptive effect
High selective pressure + high disruption effect
parent
parent

4.  Chromosomes are alowed to crossover if and only
their Hamming Distance is above the threshold
value.
 The threshold self-adapts its initial value, and varies
during the run according to the population diversity
1111111111111111
1111111100001111
Hamming dist.: 4selection
ADMGA differs from the SGA at the recombination stage
4
the number of positions at which the
corresponding symbols are different
Adaptive Dissortative MatingAdaptive Dissortative Mating
GA (ADMGA)GA (ADMGA)

5. ADMGAADMGA
Population
New population = Offspring
population + best parents
Selects two and
computes h.d.
if h. d. > ts
if h. d. ≤ ts
Crossover and mutate
after n/2 (n is the population size)
Updates threshold
if (failed matings > successful matings) ts←
ts−1
else ts ← ts+1
5
diversity is controlling the threshold
population-wide elitism (or steady-state)

6. Stationary Fitness Functions:Stationary Fitness Functions:
Scalability with Trap FunctionsScalability with Trap Functions
order-2 (k = 2) order-3 order-4
6
non-deceptive nearly-deceptive fully deceptive
Scalability with problem size

7. Alternative Replacement StrategiesThreshold ValueThreshold Value
Initial threshold
value
n = 10,000; l = 10
n = 10; l = 10,000
n = 100
order-2

8. Dynamic OptimizationDynamic Optimization
ProblemsProblems
8

9. ADMGA: DynamicADMGA: Dynamic
Optimization ProblemsOptimization Problems
 Better performance on “slower”
dynamic problems
 The performance degrades as the
optimum moves faster
9

10. MotivationMotivation
 Improve ADMGA’s performance on
faster problems
 Is population-wide elitism a good or
bad strategy for fast dynamic
problems?
10

11. Replacement StrategiesReplacement Strategies
 RS 1: Original
 RS 2: Mutated copies of the old solutions
 RS 3: Mutated copies of the best solution
 RS 4: Random Immigrants (random solutions)
11

12. ADMGA: DynamicADMGA: Dynamic
Optimization ProblemsOptimization Problems
 Yang’s (2003) dynamic problem
generator:
• frequency of change (1/ε)
• severity (ρ)
12
 ε : 600, 1200, 2400, 4800, 9600, 19200, 38400
 ρ : random
 Offline performance: average of the best fitness throughout the run
 Statistical tests

13. TestsTests
 Several mutation probability and
population size values.
• mutation: dissortative mating affects optimal
probability
• population size: avoid extra computational effort
 binary tournament
 2-elitism
 uniform crossover (p=1.0)
• Balance disruptive effect and selective pressure
13

14. Results TestsTests
RS 1 vs GGA
RS 2 vs GGA
ε→ 600 1200 2400 4800 9600 19200 38400
onemax − − ≈ ≈ ≈ ≈ ≈
trap ≈ ≈ + + + + +
knapsack − − ≈ ≈ ≈ + +
ε→ 600 1200 2400 4800 9600 19200 38400
onemax − − − − ≈ ≈ ≈
trap − − − ≈ + + +
knapsack − − − − − ≈ +

15. Results TestsTests
RS 2 vs EIGA
ε→ 600 1200 2400 4800 9600 19200 38400
onemax − − − ≈ ≈ ≈ ≈
trap ≈ ≈ + + + + +
knapsack − − ≈ ≈ ≈ ≈ ≈

16. Genetic Diverstiy

17. Conclusions and Future Work
Mutating old solutions speeds up AMDGA on dynamic problems
Only two parameters need to be adjusted: population size and
mutation rate
ADMGA is at least competitive with EIGA
Performance according to severity
Constrained Dynamic Problems