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Parallel Genetic Algorithms with Distributed-Environment Multiple Population SchemePowerPoint Presentation

Parallel Genetic Algorithms with Distributed-Environment Multiple Population Scheme

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Parallel Genetic Algorithms with Distributed-Environment Multiple Population Scheme

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Parallel Genetic Algorithms with Distributed-Environment Multiple Population Scheme. M.Miki T.Hiroyasu K.Hatanaka. Doshisha University,Kyoto,Japan. Outline. Background Optimization Problems Effects of GA Parameters Distributed GA Distributed Environment GA Conclusion.

Parallel Genetic Algorithms with Distributed-Environment Multiple Population Scheme

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Parallel Genetic Algorithms with Distributed-Environment Multiple Population Scheme

M.Miki

T.Hiroyasu

K.Hatanaka

Doshisha University,Kyoto,Japan

- Background
- Optimization Problems
- Effects of GA Parameters
- Distributed GA
- Distributed Environment GA
- Conclusion

Disadvantage

Crossover rate

Mutation rate

Effective for 1 and 2

1) High Computation Cost

2) Convergence to local minimum

3) Difficult to choose proper GA parameters

Parallel

and

Distributed Scheme

Problem on proper setting of GA parameters

Propose a new parameter-free distributed GA

The performance of GA heavily depends on the GA parameters

Proper values of GA Parameters depend on problems

Distributed Environment Scheme

5KN

Objective

Minimization of Truss Volume

Design Valuables

Sectional area ofeach member

5

6

5KN

Constraints

3

4

- Tensile Strength
- Compressive buckling
- Displacement at node 6

1

2

10-Member Truss

Design Variables

Constraint on displacement

Constraint on

tensile stress

Constraint onCompressive buckling

Sectional area of

each member

(circular shape)

12Bit ×10 = 120Bits

MutationRate

9 Combinations applied to SPGA

Experiment

9 combinations

(3 mutation rates ×3 crossover rates)

Comparison

based on the average of 10 trials

out of 12 trials omitting

the highest and the lowest values

0.1/L

10/L

1/L

0.3

0.3

0.3

0.3

CrossoverRate

10/L

0.1/L

1/L

0.6

0.6

0.6

0.6

0.1/L

1/L

10/L

1.0

1.0

1.0

1.0

0.1/L

1/L

10/L

L is the length of the chromosome

Roulette selection

Conservation of Elite

Up to 1000 generations

Pop. Size 270,2430

Crossover

Rate

Mutation

Rate

Crossover

Rate

Mutation

Rate

Mutation Rate

0.1/L

Mutation Rate

1/L

Mutation Rate

10/L

The performance of SPGA

depends heavily in

the proper choice of GA parameters

MPGA

MPGA

SPGA

Population

GA

GA

GA

GA

GA

GA

GA

GA

GA

GA

A GA is performed in one entire population.

Same GAs are performed in multiple sub population

SPGA

GA

GA

GA

GA

GA

MPGA

Slow

Fast

Migration

Experiment

Problem : Same as SPGA

MPGA:9 sub populations

Migration rate = 0.3

Migration interval = 50

[generations]

Exchange of individuals among sub populations.

Worse

Better

Randomly selected source and destination sub populations

Migration Rate

Migration interval

Mutation Rate

0.1/L

Mutation Rate

1/L

Mutation Rate

10/L

Mutation Rate

0.1/L

Mutation Rate

1/L

Mutation Rate

10/L

Mutation Rate

0.1/L

Mutation Rate

1/L

Mutation Rate

10/L

Crossover

Rate

Increase in the quality of Solutions.

However,

proper setting of

GA parameters is necessary.

Mutation

Rate

Crossover Rate

Mutation Rate

Distributed Environment GA

Conventional Environment GA

Experiment

Problem : Same as MPGA

9 Different environments

(3 mutation rates ×3 crossover rates)

for evaluation

Same parameters are used.

Different GA parameters are used.

1.75

Crossover

Rate

Best = 1.78

Results

Best = 1.74

Avg.

1.70

1. DEGA outperforms the best SPGA.

2.DEGA provides good performance even comparing to MPGA

Avg.

1.58

Worst = 1.58

Pop.size = 270

Worst = 1.38

Mutation

Rate

(1) The multiple population GA yields better solutions than single population GA because the diversity of individuals are maintained in the multiple population GA during the evolutional process.

(2) The distributed environment scheme in the multiple population GA shows a good performance compared to other conventional GA. This scheme does not need to predetermine the GA parameters,and it is very useful for many problems where the proper values of those parameters are not known.