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1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUANTUM-INSPIRED EVOLUTIONARY
ALGORITHMS IN SEARCH AND OPTIMIZATION
Robert Nowotniak, MSc
Supervisor: Prof. Jacek Kucharski
Computer Engineering DepartmentThe Faculty of Electrical, Electronic, Computer and Control Engineering
Technical University of Lodz
Rogow, April 17-19, 2011
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
PRESENTATION OUTLINE
1 Artificial Intelligence2 Quantum Computing3 Quantum-Inspired Evolutionary Algorithms4 Selected Applications and Results
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 1 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
PRESENTATION OUTLINE
1 Artificial Intelligence2 Quantum Computing3 Quantum-Inspired Evolutionary Algorithms4 Selected Applications and Results
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 1 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
ARTIFICIAL INTELLIGENCE
Major subfields in Artificial Intelligence:1 Artificial Neural Networks2 Fuzzy Systems3 Evolutionary Computing4 Artificial Immune Systems5 Collective Intelligence6 Machine Learning7 ...
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 2 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
ARTIFICIAL INTELLIGENCE
Major subfields in Artificial Intelligence:1 Artificial Neural Networks2 Fuzzy Systems3 Evolutionary Computing4 Artificial Immune Systems5 Collective Intelligence6 Machine Learning7 ...
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 2 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
ARTIFICIAL INTELLIGENCE
Major subfields in Artificial Intelligence:1 Artificial Neural Networks2 Fuzzy Systems3 Evolutionary Computing4 Artificial Immune Systems5 Collective Intelligence6 Machine Learning7 ...
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 2 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SIMPLE GENETIC ALGORITHM
Solutions to technical problems can be encoded asbinary 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
populationof solutions
— chromosome
— binary gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 3 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SIMPLE GENETIC ALGORITHM
Solutions to technical problems can be encoded asbinary 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
populationof solutions
— chromosome
— binary gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 3 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SIMPLE GENETIC ALGORITHM
Solutions to technical problems can be encoded asbinary 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
populationof solutions
— chromosome
— binary gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 3 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SIMPLE GENETIC ALGORITHM
Solutions to technical problems can be encoded asbinary 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
populationof solutions
— chromosome
— binary gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 3 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SIMPLE GENETIC ALGORITHM
Solutions to technical problems can be encoded asbinary 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
populationof solutions
— chromosome
— binary gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 3 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SIMPLE GENETIC ALGORITHM
Solutions to technical problems can be encoded asbinary 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
populationof solutions
— chromosome
— binary gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 3 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SIMPLE GENETIC ALGORITHM
Solutions to technical problems can be encoded asbinary 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
populationof solutions
— chromosome
— binary gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 3 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SIMPLE GENETIC ALGORITHM
1 Representation of solutions
2 Initialization3 Genetic operators4 Evaluation
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 4 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SIMPLE GENETIC ALGORITHM
1 Representation of solutions2 Initialization3 Genetic operators4 Evaluation
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 4 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SIMPLE GENETIC ALGORITHM
1 Representation of solutions
2 Initialization3 Genetic operators4 Evaluation
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 4 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUANTUM COMPUTING
Quantum Computing – branch of theoreticalcomputer science dealing with application ofquantum mechanical effects to solvingcomputational problems.
Selected quantum mechanical phenomena:1 quantum entanglement2 superposition of states3 interference4 probability amplitudes5 parallelism
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 5 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUANTUM COMPUTING
Quantum Computing – branch of theoreticalcomputer science dealing with application ofquantum mechanical effects to solvingcomputational problems.
Selected quantum mechanical phenomena:1 quantum entanglement2 superposition of states3 interference4 probability amplitudes5 parallelism
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 5 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
CLASSICAL BITS VS QUBITS
Geometrical representation of Qubit on the Bloch sphereRobert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 6 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
CLASSICAL BITS VS QUBITS
Geometrical representation of Qubit on the Bloch sphereRobert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 6 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
CLASSICAL BITS VS QUBITS
Geometrical representation of Qubit on the Bloch sphereRobert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 6 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUANTUM-INSPIRED EVOLUTIONARY ALGORITHMS
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 7 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUANTUM-INSPIRED EVOLUTIONARY ALGORITHMS
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 7 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUBITS AND BINARY QUANTUM GENES
|ψ〉 =√32︸︷︷︸α
|0〉+ 12︸︷︷︸β
|1〉
|0〉
|1〉
|ψ〉
α
β
qubit (quantum bit): |ψ〉 = α|0〉+ β|1〉where: α, β ∈ C, |α|2 + |β|2 = 1Pr|ψ〉 : F{0,1} 7→ [0,1]Pr|ψ〉({0}) = |α|2
Pr|ψ〉({1}) = |β|2
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 8 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUBITS AND BINARY QUANTUM GENES
|ψ〉 =√22︸︷︷︸α
|0〉+√22︸︷︷︸β
|1〉
|0〉
|1〉
|ψ〉
α
β
qubit (quantum bit): |ψ〉 = α|0〉+ β|1〉where: α, β ∈ C, |α|2 + |β|2 = 1Pr|ψ〉 : F{0,1} 7→ [0,1]Pr|ψ〉({0}) = |α|2
Pr|ψ〉({1}) = |β|2
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 8 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUBITS AND BINARY QUANTUM GENES
|ψ〉 = 13︸︷︷︸α
|0〉+ 2√23︸ ︷︷ ︸β
|1〉
|0〉
|1〉|ψ〉
α
β
qubit (quantum bit): |ψ〉 = α|0〉+ β|1〉where: α, β ∈ C, |α|2 + |β|2 = 1Pr|ψ〉 : F{0,1} 7→ [0,1]Pr|ψ〉({0}) = |α|2
Pr|ψ〉({1}) = |β|2
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 8 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUBITS AND BINARY QUANTUM GENES
|ψ〉 = 0︸︷︷︸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 = 1Pr|ψ〉 : F{0,1} 7→ [0,1]Pr|ψ〉({0}) = |α|2
Pr|ψ〉({1}) = |β|2
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 8 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SITUATION FOR SIMPLE GENETIC ALGORITHM
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
populationof solutions
— chromosome
— binary gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 9 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SITUATION FOR SIMPLE GENETIC ALGORITHM
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
populationof solutions
— chromosome
— binary gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 9 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SITUATION FOR SIMPLE GENETIC ALGORITHM
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
populationof solutions
— chromosome
— binary gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 9 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
ILLUSTRATION OF QUANTUM POPULATION
quantumpopulation
— quantum chromosome
— quantum gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 10 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
ILLUSTRATION OF QUANTUM POPULATION
quantumpopulation
— quantum chromosome
— quantum gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 10 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
ILLUSTRATION OF QUANTUM POPULATION
quantumpopulation
— quantum chromosome
— quantum gene
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 10 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUANTUM-INSPIRED EVOLUTIONARY ALGORITHMS
Quantum-inspired elements bring a ”new dimension” intoEvolutionary Algorithms.
CURRENT PROBLEMS
1 How to use the ”new dimension” efficiently?2 Theoretical aspects of QIEAs have not been studied with
due attention.3 No general rules and guidelines for constructing QIEAs
have been identified.
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 11 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUANTUM-INSPIRED EVOLUTIONARY ALGORITHMS
Quantum-inspired elements bring a ”new dimension” intoEvolutionary Algorithms.
CURRENT PROBLEMS
1 How to use the ”new dimension” efficiently?
2 Theoretical aspects of QIEAs have not been studied withdue attention.
3 No general rules and guidelines for constructing QIEAshave been identified.
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 11 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUANTUM-INSPIRED EVOLUTIONARY ALGORITHMS
Quantum-inspired elements bring a ”new dimension” intoEvolutionary Algorithms.
CURRENT PROBLEMS
1 How to use the ”new dimension” efficiently?2 Theoretical aspects of QIEAs have not been studied with
due attention.
3 No general rules and guidelines for constructing QIEAshave been identified.
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 11 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
QUANTUM-INSPIRED EVOLUTIONARY ALGORITHMS
Quantum-inspired elements bring a ”new dimension” intoEvolutionary Algorithms.
CURRENT PROBLEMS
1 How to use the ”new dimension” efficiently?2 Theoretical aspects of QIEAs have not been studied with
due attention.3 No general rules and guidelines for constructing QIEAs
have been identified.
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 11 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
THE PH.D. DISSERTATION
Analysis of Quantum-Inpsired Evolutionary Algorithms(to be finished by the end of 2011)
ORIGINAL CONTRIBUTIONS
1 Convergence analysis of Quantum-Inspired EvolutionaryAlgorithms based on Banach’s fixed point theorem
2 Building blocks propagation analysisin Quantum-Inspired Genetic Algorithms[1]
3 Implementation in massively parallel computingenvironment (nVidia CUDATM technology)
4 Tuning QIEAs: meta-optimization[2]
1Nowotniak, R., Kucharski, J.: Building Blocks Propagation in Quantum-InspiredGenetic Algorithm, Scientific Bulletin of Academy of Science and Technology, 2011
2Nowotniak, R., Kucharski, J.: Meta-optimization of Quantum-Inspired EvolutionaryAlgorithm, XVII International Conference on Information Technology Systems, 2010
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 12 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
THE PH.D. DISSERTATION
Analysis of Quantum-Inpsired Evolutionary Algorithms(to be finished by the end of 2011)
ORIGINAL CONTRIBUTIONS
1 Convergence analysis of Quantum-Inspired EvolutionaryAlgorithms based on Banach’s fixed point theorem
2 Building blocks propagation analysisin Quantum-Inspired Genetic Algorithms[1]
3 Implementation in massively parallel computingenvironment (nVidia CUDATM technology)
4 Tuning QIEAs: meta-optimization[2]
1Nowotniak, R., Kucharski, J.: Building Blocks Propagation in Quantum-InspiredGenetic Algorithm, Scientific Bulletin of Academy of Science and Technology, 2011
2Nowotniak, R., Kucharski, J.: Meta-optimization of Quantum-Inspired EvolutionaryAlgorithm, XVII International Conference on Information Technology Systems, 2010
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 12 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
THE PH.D. DISSERTATION
Analysis of Quantum-Inpsired Evolutionary Algorithms(to be finished by the end of 2011)
ORIGINAL CONTRIBUTIONS
1 Convergence analysis of Quantum-Inspired EvolutionaryAlgorithms based on Banach’s fixed point theorem
2 Building blocks propagation analysisin Quantum-Inspired Genetic Algorithms[1]
3 Implementation in massively parallel computingenvironment (nVidia CUDATM technology)
4 Tuning QIEAs: meta-optimization[2]
1Nowotniak, R., Kucharski, J.: Building Blocks Propagation in Quantum-InspiredGenetic Algorithm, Scientific Bulletin of Academy of Science and Technology, 2011
2Nowotniak, R., Kucharski, J.: Meta-optimization of Quantum-Inspired EvolutionaryAlgorithm, XVII International Conference on Information Technology Systems, 2010
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 12 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
THE PH.D. DISSERTATION
Analysis of Quantum-Inpsired Evolutionary Algorithms(to be finished by the end of 2011)
ORIGINAL CONTRIBUTIONS
1 Convergence analysis of Quantum-Inspired EvolutionaryAlgorithms based on Banach’s fixed point theorem
2 Building blocks propagation analysisin Quantum-Inspired Genetic Algorithms[1]
3 Implementation in massively parallel computingenvironment (nVidia CUDATM technology)
4 Tuning QIEAs: meta-optimization[2]
1Nowotniak, R., Kucharski, J.: Building Blocks Propagation in Quantum-InspiredGenetic Algorithm, Scientific Bulletin of Academy of Science and Technology, 2011
2Nowotniak, R., Kucharski, J.: Meta-optimization of Quantum-Inspired EvolutionaryAlgorithm, XVII International Conference on Information Technology Systems, 2010
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 12 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
THE PH.D. DISSERTATION
Analysis of Quantum-Inpsired Evolutionary Algorithms(to be finished by the end of 2011)
ORIGINAL CONTRIBUTIONS
1 Convergence analysis of Quantum-Inspired EvolutionaryAlgorithms based on Banach’s fixed point theorem
2 Building blocks propagation analysisin Quantum-Inspired Genetic Algorithms[1]
3 Implementation in massively parallel computingenvironment (nVidia CUDATM technology)
4 Tuning QIEAs: meta-optimization[2]
1Nowotniak, R., Kucharski, J.: Building Blocks Propagation in Quantum-InspiredGenetic Algorithm, Scientific Bulletin of Academy of Science and Technology, 2011
2Nowotniak, R., Kucharski, J.: Meta-optimization of Quantum-Inspired EvolutionaryAlgorithm, XVII International Conference on Information Technology Systems, 2010
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 12 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
THE PH.D. DISSERTATION
Analysis of Quantum-Inpsired Evolutionary Algorithms(to be finished by the end of 2011)
ORIGINAL CONTRIBUTIONS
1 Convergence analysis of Quantum-Inspired EvolutionaryAlgorithms based on Banach’s fixed point theorem
2 Building blocks propagation analysisin Quantum-Inspired Genetic Algorithms[1]
3 Implementation in massively parallel computingenvironment (nVidia CUDATM technology)
4 Tuning QIEAs: meta-optimization[2]
1Nowotniak, R., Kucharski, J.: Building Blocks Propagation in Quantum-InspiredGenetic Algorithm, Scientific Bulletin of Academy of Science and Technology, 2011
2Nowotniak, R., Kucharski, J.: Meta-optimization of Quantum-Inspired EvolutionaryAlgorithm, XVII International Conference on Information Technology Systems, 2010
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 12 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SELECTED APPLICATIONS
1 Simultaneous Localization andMapping (SLAM) problem for mobilerobots[3]
2 Segmentation of titanium alloysimages obtained with X-Raymicrotomography[4]
3Jezewski, S., Łaski, M., Nowotniak, R.: Comparison of Algorithms forSimultaneous Localization and Mapping Problem for Mobile Robot, Scientific Bulletin ofAcademy of Science and Technology,. Automatics, 2011
4Jopek, Ł., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Applicationof Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin ofAcademy of Science and Technology, Automatics, 2010
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 13 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SELECTED APPLICATIONS
1 Simultaneous Localization andMapping (SLAM) problem for mobilerobots[3]
2 Segmentation of titanium alloysimages obtained with X-Raymicrotomography[4]
3Jezewski, S., Łaski, M., Nowotniak, R.: Comparison of Algorithms forSimultaneous Localization and Mapping Problem for Mobile Robot, Scientific Bulletin ofAcademy of Science and Technology,. Automatics, 2011
4Jopek, Ł., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Applicationof Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin ofAcademy of Science and Technology, Automatics, 2010
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 13 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SELECTED APPLICATIONS
1 Simultaneous Localization andMapping (SLAM) problem for mobilerobots[3]
2 Segmentation of titanium alloysimages obtained with X-Raymicrotomography[4]
3Jezewski, S., Łaski, M., Nowotniak, R.: Comparison of Algorithms forSimultaneous Localization and Mapping Problem for Mobile Robot, Scientific Bulletin ofAcademy of Science and Technology,. Automatics, 2011
4Jopek, Ł., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Applicationof Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin ofAcademy of Science and Technology, Automatics, 2010
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 13 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
SELECTED APPLICATIONS
1 Simultaneous Localization andMapping (SLAM) problem for mobilerobots[3]
2 Segmentation of titanium alloysimages obtained with X-Raymicrotomography[4]
3Jezewski, S., Łaski, M., Nowotniak, R.: Comparison of Algorithms forSimultaneous Localization and Mapping Problem for Mobile Robot, Scientific Bulletin ofAcademy of Science and Technology,. Automatics, 2011
4Jopek, Ł., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Applicationof Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin ofAcademy of Science and Technology, Automatics, 2010
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 13 / 14
1. Artificial Intelligence 2. Quantum Computing 3. Quantum-Inspired Evolutionary Algorithms
MY RECENT PAPERS
1 R.Nowotniak, J. Kucharski, Meta-optimization of Quantum-InspiredEvolutionary Algorithm, 2010, Proceedings of the XVII InternationalConference on Information Technology Systems,ISBN 978-83-7283-378-5
2 R.Nowotniak, J. Kucharski, Building Blocks Propagation inQuantum-Inspired Genetic Algorithm, 2010, Scientific Bulletin ofAcademy 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.Jezewski, M. Łaski, R. Nowotniak, Comparison of Algorithms forSimultaneous 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 SelectionProblem, 2009, Scientific Bulletin of Academy of Science andTechnology, Automatics, ISSN 1429-3447
Robert Nowotniak Quantum-Inspired Evolutionary Algorithms in Search and Optimization 14 / 14