[PPT] - | 1 Pr | ( { 1 } ) = | | 2 3 | 0 + 1 | = | 1 | 2 2 PowerPoint Presentation

SLIDE 1

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

GPU-BASED TUNING OF QUANTUM-INSPIRED GENETIC ALGORITHM FOR A COMBINATORIAL OPTIMIZATION PROBLEM

Robert Nowotniak, Jacek Kucharski

Computer Engineering Department The Faculty of Electrical, Electronic, Computer and Control Engineering Technical University of Lodz

XIV INTERNATIONAL CONFERENCE SYSTEM MODELLING and CONTROL June 27-29, 2011 Ł´

d´

z

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 1 / 19

SLIDE 2

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

PRESENTATION OUTLINE

1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDATM TECHNOLOGY 3 TUNING – EXPERIMENTAL RESULTS 4 SUMMARY

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011

SLIDE 3

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

PRESENTATION OUTLINE

1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDATM TECHNOLOGY 3 TUNING – EXPERIMENTAL RESULTS 4 SUMMARY

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011

SLIDE 4

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QUANTUM-INSPIRED GENETIC ALGORITHMS

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 2 / 19

SLIDE 5

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QUANTUM-INSPIRED GENETIC ALGORITHMS

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 2 / 19

SLIDE 6

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QUANTUM ELEMENTS IN EVOLUTIONARY ALGORITHMS

1 Representation of solutions

Instead of exact points in a search space, probability distributions of sampling the space

2 Initialization 3 Genetic operators 4 Evaluation

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 3 / 19

SLIDE 7

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QUANTUM ELEMENTS IN EVOLUTIONARY ALGORITHMS

1 Representation of solutions (bits → qubits)

Instead of exact points in a search space, probability distributions of sampling the space

2 Initialization 3 Genetic operators 4 Evaluation

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 3 / 19

SLIDE 8

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QUANTUM ELEMENTS IN EVOLUTIONARY ALGORITHMS

1 Representation of solutions (bits → qubits)

Instead of exact points in a search space, probability distributions of sampling the space

2 Initialization 3 Genetic operators 4 Evaluation

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 3 / 19

SLIDE 9

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

CLASSICAL BITS VS QUBITS

Geometrical representation of Qubit on the Bloch sphere

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 4 / 19

SLIDE 10

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

CLASSICAL BITS VS QUBITS

Geometrical representation of Qubit on the Bloch sphere

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 4 / 19

SLIDE 11

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

CLASSICAL BITS VS QUBITS

Geometrical representation of Qubit on the Bloch sphere

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 4 / 19

SLIDE 12

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QUBITS AND BINARY QUANTUM GENES

|ψ = √ 3 2

α

|0 + 1 2

β

|1 |0 |1 |ψ α β

qubit (quantum bit): |ψ = α|0 + β|1 where: α, β ∈ C, |α|2 + |β|2 = 1 Pr|ψ : F{0,1} → [0, 1] Pr|ψ({0}) = |α|2 Pr|ψ({1}) = |β|2

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 5 / 19

SLIDE 13

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QUBITS AND BINARY QUANTUM GENES

|ψ = √ 2 2

α

|0 + √ 2 2

β

|1 |0 |1 |ψ α β

qubit (quantum bit): |ψ = α|0 + β|1 where: α, β ∈ C, |α|2 + |β|2 = 1 Pr|ψ : F{0,1} → [0, 1] Pr|ψ({0}) = |α|2 Pr|ψ({1}) = |β|2

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 5 / 19

SLIDE 14

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QUBITS AND BINARY QUANTUM GENES

|ψ = 1 3

α

|0 + 2 √ 2 3

β

|1 |0 |1 |ψ α β

qubit (quantum bit): |ψ = α|0 + β|1 where: α, β ∈ C, |α|2 + |β|2 = 1 Pr|ψ : F{0,1} → [0, 1] Pr|ψ({0}) = |α|2 Pr|ψ({1}) = |β|2

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 5 / 19

SLIDE 15

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QUBITS AND BINARY QUANTUM GENES

|ψ =

α

|0 + 1

β

|1 |0 |1 |ψ α β

qubit (quantum bit): |ψ = α|0 + β|1 where: α, β ∈ C, |α|2 + |β|2 = 1 Pr|ψ : F{0,1} → [0, 1] Pr|ψ({0}) = |α|2 Pr|ψ({1}) = |β|2

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 5 / 19

SLIDE 16

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SIMPLE GENETIC ALGORITHM

In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: 1 1 0 1 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1

                      

population

f solutions

— chromosome — binary gene

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 6 / 19

SLIDE 17

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SIMPLE GENETIC ALGORITHM

In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: 1 1 0 1 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1

                      

population

f solutions

— chromosome — binary gene

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 6 / 19

SLIDE 18

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SIMPLE GENETIC ALGORITHM

In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: 1 1 0 1 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1

                      

population

f solutions

— chromosome — binary gene

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 6 / 19

SLIDE 19

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SIMPLE GENETIC ALGORITHM

In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: 1 1 0 1 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 1

                      

population

f solutions

— chromosome — binary gene

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 6 / 19

SLIDE 20

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SIMPLE GENETIC ALGORITHM

In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: 0 0 0 0 1 0 0 1 0 0 1 0 1 1 1 1 1 0 0 1 0 1 1 0 0 0 1 0 0 0 1 0 1 1 0

                      

population

f solutions

— chromosome — binary gene

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 6 / 19

SLIDE 21

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SIMPLE GENETIC ALGORITHM

In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: 0 0 0 0 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 1 0 1 0 1 1 1 1 0 1 0 1 1

                      

population

f solutions

— chromosome — binary gene

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 6 / 19

SLIDE 22

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SIMPLE GENETIC ALGORITHM

In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1 0

                      

population

f solutions

— chromosome — binary gene

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 6 / 19

SLIDE 23

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QUANTUM-INSPIRED GENETIC ALGORITHMS

In Quantum-Inspired Genetic Algorithms, each individual encodes probability distribution of sampling the search space

                          

quantum population — quantum chromosome — quantum gene

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 7 / 19

SLIDE 24

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QUANTUM-INSPIRED GENETIC ALGORITHMS

In Quantum-Inspired Genetic Algorithms, each individual encodes probability distribution of sampling the search space

                          

quantum population — quantum chromosome — quantum gene

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 7 / 19

SLIDE 25

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QUANTUM-INSPIRED GENETIC ALGORITHMS

In Quantum-Inspired Genetic Algorithms, each individual encodes probability distribution of sampling the search space

                          

quantum population — quantum chromosome — quantum gene

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 7 / 19

SLIDE 26

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

PRESENTATION OUTLINE

1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDATM TECHNOLOGY 3 TUNING – EXPERIMENTAL RESULTS 4 SUMMARY

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011

SLIDE 27

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

PRESENTATION OUTLINE

1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDATM TECHNOLOGY 3 TUNING – EXPERIMENTAL RESULTS 4 SUMMARY

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011

SLIDE 28

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

NVIDIA R TESLATM S1070-400

TeslaTM s1070-400 consists of: 4 CUDA GPU cards, each:

30 streaming multiprocessors (SMs)

8 cores each (separate ALUs) 16 KB of shared memory highly effective (zero-overhead) tasks scheduler

4 GB global memory

Total: 4 ∗ 30 ∗ 8 = 960 processor cores

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 8 / 19

SLIDE 29

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

NVIDIA R TESLATM S1070-400

TeslaTM s1070-400 consists of: 4 CUDA GPU cards, each:

30 streaming multiprocessors (SMs)

8 cores each (separate ALUs) 16 KB of shared memory highly effective (zero-overhead) tasks scheduler

4 GB global memory

Total: 4 ∗ 30 ∗ 8 = 960 processor cores

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 8 / 19

SLIDE 30

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

THREAD HIERARCHY ON CUDATM GPU

In CUDA, threads are grouped in blocks and blocks constitute a grid. The unit of thread scheduling is warp (32 threads). Grid of Thread Blocks

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 9 / 19

SLIDE 31

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

PROPOSED APPROACH TO PARALLELIZATION

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 10 / 19

SLIDE 32

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

GPU-BASED IMPLEMENTATION OF QIGA

Two levels:

1 Coarse-grained parallelization

In a grid, there can be several hundred blocks evolving independent populations with same or different parameters simultaneously.

2 Fine-grained parallelization

On the population level, each individual can be evaluated and transformed in a separate GPU thread. Thus, the whole population can be represented as a block of threads. Hundreds of populations with same or different parameters can be evolved in parallel, simultaneously

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 11 / 19

SLIDE 33

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

GPU-BASED IMPLEMENTATION OF QIGA

Two levels:

1 Coarse-grained parallelization

In a grid, there can be several hundred blocks evolving independent populations with same or different parameters simultaneously.

2 Fine-grained parallelization

On the population level, each individual can be evaluated and transformed in a separate GPU thread. Thus, the whole population can be represented as a block of threads. Hundreds of populations with same or different parameters can be evolved in parallel, simultaneously

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 11 / 19

SLIDE 34

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

PRESENTATION OUTLINE

1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDATM TECHNOLOGY 3 TUNING – EXPERIMENTAL RESULTS 4 SUMMARY

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011

SLIDE 35

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

PRESENTATION OUTLINE

1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDATM TECHNOLOGY 3 TUNING – EXPERIMENTAL RESULTS 4 SUMMARY

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011

SLIDE 36

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

NUMERICAL EXPERIMENT

TEST: COMBINATORIAL OPTIMIZATION Knapsack problem (hard version, strongly correlated set of items) Number of items: 250 Comparison:

1 Simple Genetic Algorithm (SGA)

popsize = 100, Pc = 0.65, Pm = 0.05

2 Quantum-Inspired Genetic Algorithm (QIGA)

popsize = 10, other parameters (rotation angles) as in [1]

3 Tuned Quantum-Inpsired Genetic Algorithm

1Han, K. H., Kim, J. H.: Genetic quantum algorithm and its application to

combinatorial optimization problem. Proceedings of the 2000 Congress on Evolutionary computation, 2000

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 12 / 19

SLIDE 37

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

NUMERICAL EXPERIMENT

TEST: COMBINATORIAL OPTIMIZATION Knapsack problem (hard version, strongly correlated set of items) Number of items: 250 Comparison:

1 Simple Genetic Algorithm (SGA)

popsize = 100, Pc = 0.65, Pm = 0.05

2 Quantum-Inspired Genetic Algorithm (QIGA)

popsize = 10, other parameters (rotation angles) as in [1]

3 Tuned Quantum-Inpsired Genetic Algorithm

1Han, K. H., Kim, J. H.: Genetic quantum algorithm and its application to

combinatorial optimization problem. Proceedings of the 2000 Congress on Evolutionary computation, 2000

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 12 / 19

SLIDE 38

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QIGA EXECUTION TIME COMPARISON

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 13 / 19

SLIDE 39

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

QIGA EXECUTION TIME COMPARISON

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 13 / 19

SLIDE 40

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SPEEDUP ON CUDATM

1 Pentium-III 500MHz (Visual C++ 6.0)

0.723 experiments / second (according to [1])

2 Intel Core i7 2.93GHz (1 core, ANSI C)

7.33 experiments / second

3 NVidia GTX 295 (CUDA C)

890 experiments / second (about 120x speedup)

4 8 GPUs (GTX295+GTX285+Tesla s1070+Tesla C2070)

3089 experiments / second (over 400x speedup) The speedup gained allows efficient meta-optimization (parameters tuning) of the algorithms

1Han, K. H., Kim, J. H.: Genetic quantum algorithm and its application to

combinatorial optimization problem. Proceedings of the 2000 Congress on Evolutionary computation, 2000

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 14 / 19

SLIDE 41

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SPEEDUP ON CUDATM

1 Pentium-III 500MHz (Visual C++ 6.0)

0.723 experiments / second (according to [1])

2 Intel Core i7 2.93GHz (1 core, ANSI C)

7.33 experiments / second

3 NVidia GTX 295 (CUDA C)

890 experiments / second (about 120x speedup)

4 8 GPUs (GTX295+GTX285+Tesla s1070+Tesla C2070)

3089 experiments / second (over 400x speedup) The speedup gained allows efficient meta-optimization (parameters tuning) of the algorithms

1Han, K. H., Kim, J. H.: Genetic quantum algorithm and its application to

combinatorial optimization problem. Proceedings of the 2000 Congress on Evolutionary computation, 2000

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 14 / 19

SLIDE 42

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

META-OPTIMIZATION (PARAMETERS TUNING)

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 15 / 19

SLIDE 43

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

RESULTS OF META-OPTIMIZATION

Meta-fitness of the algorithm: knapsack profit at the end of evolution Subject to meta-optimization: rotation angles in quantum genes state space rotation angles θ meta-fitness 0.000 0.038 0.349 0.334 0.349 1458.86 0.117 0.036 0.349 0.349 0.000 1458.79 0.063 0.038 0.239 0.326 0.320 1458.14 0.157 0.034 0.256 0.349 0.081 1456.82 0.281 0.032 0.206 0.348 0.137 1456.09 0.157 0.031 0.079 0.016 0.079 1408.25[1]

1Han, K. H., Kim, J. H.: Genetic quantum algorithm and its application to

combinatorial optimization problem. Proceedings of the 2000 Congress on Evolutionary computation, 2000

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 16 / 19

SLIDE 44

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

PERFORMANCE COMPARISON

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 17 / 19

SLIDE 45

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

PRESENTATION OUTLINE

1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDATM TECHNOLOGY 3 TUNING – EXPERIMENTAL RESULTS 4 SUMMARY

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011

SLIDE 46

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

PRESENTATION OUTLINE

1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDATM TECHNOLOGY 3 TUNING – EXPERIMENTAL RESULTS 4 SUMMARY

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011

SLIDE 47

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SUMMARY

In our research:

1 Quantum-Inspired Genetic Algorithm has been

implemented in NVidia CUDATM technology

2 Over 400x speedup has been gained on 8 GPU devices 3 The speedup allows efficient meta-optimization of selected

parameters (rotation angles in quantum genes state space)

4 Real-Coded Evolutionary Algorithm has been used as

an overlaid meta-optimizer

5 Tuned QIGA algorithm performs much better in the

considered combinatorial optimization problem

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 18 / 19

SLIDE 48

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

MY SELECTED RECENT PAPERS

1

R.Nowotniak, J. Kucharski, Meta-optimization of Quantum-Inspired Evolutionary Algorithm, 2010, Proceedings of the XVII International Conference on Information Technology Systems, ISBN 978-83-7283-378-5

2

R.Nowotniak, J. Kucharski, Building Blocks Propagation in Quantum-Inspired Genetic Algorithm, 2010, Scientific Bulletin of Academy of Science and Technology, Automatics, 2010, ISSN 1429-3447

3

R. Nowotniak, Survey of Quantum-Inspired Evolutionary Algorithms,

2010, Proceedings of the FIMB PhD students conference, ISSN 2082-4831

4

S.Je˙ zewski, M. Łaski, R. Nowotniak, Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, 2010, Scientific Bulletin of Academy of Science and Technology, Automatics, ISSN 1429-3447

5

Ł. Jopek, R. Nowotniak, M. Postolski, L. Babout, M. Janaszewski, Application of Quantum Genetic Algorithms in Feature Selection Problem, 2009, Scientific Bulletin of Academy of Science and Technology, Automatics, ISSN 1429-3447

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011 19 / 19

SLIDE 49

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

Thank you for your attention

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011

SLIDE 50

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SELECTED APPLICATIONS

1 Simultaneous Localization and

Mapping (SLAM) problem for mobile robots[2]

2 Segmentation of titanium alloys

images obtained with X-Ray microtomography[3]

2Je˙

zewski, S., Łaski, M., Nowotniak, R.: Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, Scientific Bulletin of Academy of Science and Technology,. Automatics, 2011

3Jopek, Ł., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Application

f Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin of

Academy of Science and Technology, Automatics, 2010

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011

SLIDE 51

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SELECTED APPLICATIONS

1 Simultaneous Localization and

Mapping (SLAM) problem for mobile robots[2]

2 Segmentation of titanium alloys

images obtained with X-Ray microtomography[3]

2Je˙

zewski, S., Łaski, M., Nowotniak, R.: Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, Scientific Bulletin of Academy of Science and Technology,. Automatics, 2011

3Jopek, Ł., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Application

f Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin of

Academy of Science and Technology, Automatics, 2010

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011

SLIDE 52

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SELECTED APPLICATIONS

1 Simultaneous Localization and

Mapping (SLAM) problem for mobile robots[2]

2 Segmentation of titanium alloys

images obtained with X-Ray microtomography[3]

2Je˙

zewski, S., Łaski, M., Nowotniak, R.: Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, Scientific Bulletin of Academy of Science and Technology,. Automatics, 2011

3Jopek, Ł., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Application

f Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin of

Academy of Science and Technology, Automatics, 2010

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011

SLIDE 53

1. Quantum-Inspired Genetic Algorithms
2. NVidia CUDATM
3. Experimental Results
4. Summary

SELECTED APPLICATIONS

1 Simultaneous Localization and

Mapping (SLAM) problem for mobile robots[2]

2 Segmentation of titanium alloys

images obtained with X-Ray microtomography[3]

2Je˙

zewski, S., Łaski, M., Nowotniak, R.: Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, Scientific Bulletin of Academy of Science and Technology,. Automatics, 2011

3Jopek, Ł., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Application

f Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin of

Academy of Science and Technology, Automatics, 2010

Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011