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Survey of Quantum-Inspired Evolutionary Algorithms Survey of Quantum-Inspired Evolutionary Algorithms Robert Nowotniak, MSc Supervisor: Jacek Kucharski, MSc, PhD, DSc Computer Engineering Department Technical University of Lodz L od

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SLIDE 1

Survey of Quantum-Inspired Evolutionary Algorithms

Survey of Quantum-Inspired Evolutionary Algorithms

Robert Nowotniak, MSc Supervisor: Jacek Kucharski, MSc, PhD, DSc

Computer Engineering Department Technical University of Lodz

  • L´
  • d´

z, October 20, 2010

Robert Nowotniak

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  • d´

z, October 20, 2010

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Survey of Quantum-Inspired Evolutionary Algorithms

Outline

1 Background Information and Scope of Research 2 Quantum + Evolutionary Computing 3 Quantum Elements in Evolutionary Algorithms 4 Current problems 5 My Contributions 6 Preliminary Results and Selected Applications

Robert Nowotniak

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Survey of Quantum-Inspired Evolutionary Algorithms

Quantum Computing

Quantum Computing – branch of theoretical computer science dealing with application of quantum mechanical effects to solving computational problems.

Robert Nowotniak

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Survey of Quantum-Inspired Evolutionary Algorithms

Quantum + Evolutionary Computing

Possible interplay between quantum and evolutionary computing:

1 Evolutionary-Designed Quantum Algorithms

Automatic synthesis of quantum algorithms

2 Quantum-Inspired Evolutionary Algorithms (QIEAs)

Modification of existing EA algorithms by application

  • f quantum-inspired concepts and principles

3 ,,True” Quantum Evolutionary Algorithms

Ultimately, implementation of the algorithms in a ”true” quantum level hardware is the biggest challenge for the future

Robert Nowotniak

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z, October 20, 2010 3 / 15

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SLIDE 5

Survey of Quantum-Inspired Evolutionary Algorithms

Quantum + Evolutionary Computing

Possible interplay between quantum and evolutionary computing:

1 Evolutionary-Designed Quantum Algorithms

Automatic synthesis of quantum algorithms

2 Quantum-Inspired Evolutionary Algorithms (QIEAs)

Modification of existing EA algorithms by application

  • f quantum-inspired concepts and principles

3 ,,True” Quantum Evolutionary Algorithms

Ultimately, implementation of the algorithms in a ”true” quantum level hardware is the biggest challenge for the future

Robert Nowotniak

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z, October 20, 2010 3 / 15

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SLIDE 6

Survey of Quantum-Inspired Evolutionary Algorithms

Quantum + Evolutionary Computing

Possible interplay between quantum and evolutionary computing:

1 Evolutionary-Designed Quantum Algorithms

Automatic synthesis of quantum algorithms

2 Quantum-Inspired Evolutionary Algorithms (QIEAs)

Modification of existing EA algorithms by application

  • f quantum-inspired concepts and principles

3 ,,True” Quantum Evolutionary Algorithms

Ultimately, implementation of the algorithms in a ”true” quantum level hardware is the biggest challenge for the future

Robert Nowotniak

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z, October 20, 2010 3 / 15

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SLIDE 7

Survey of Quantum-Inspired Evolutionary Algorithms

Quantum + Evolutionary Computing

Possible interplay between quantum and evolutionary computing:

1 Evolutionary-Designed Quantum Algorithms

Automatic synthesis of quantum algorithms

2 Quantum-Inspired Evolutionary Algorithms (QIEAs)

Modification of existing EA algorithms by application

  • f quantum-inspired concepts and principles

3 ,,True” Quantum Evolutionary Algorithms

Ultimately, implementation of the algorithms in a ”true” quantum level hardware is the biggest challenge for the future

Robert Nowotniak

  • L´
  • d´

z, October 20, 2010 3 / 15

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SLIDE 8

Survey of Quantum-Inspired Evolutionary Algorithms

Quantum + Evolutionary Computing

Possible interplay between quantum and evolutionary computing:

1 Evolutionary-Designed Quantum Algorithms

Automatic synthesis of quantum algorithms

2 Quantum-Inspired Evolutionary Algorithms (QIEAs)

Modification of existing EA algorithms by application

  • f quantum-inspired concepts and principles

3 ,,True” Quantum Evolutionary Algorithms

Ultimately, implementation of the algorithms in a ”true” quantum level hardware is the biggest challenge for the future

Robert Nowotniak

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z, October 20, 2010 3 / 15

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SLIDE 9

Survey of Quantum-Inspired Evolutionary Algorithms

Scope of Research

Robert Nowotniak

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z, October 20, 2010 4 / 15

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SLIDE 10

Survey of Quantum-Inspired Evolutionary Algorithms

Scope of Research

Robert Nowotniak

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z, October 20, 2010 4 / 15

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SLIDE 11

Survey of Quantum-Inspired Evolutionary Algorithms

Scope of Research

Robert Nowotniak

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z, October 20, 2010 4 / 15

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Survey of Quantum-Inspired Evolutionary Algorithms

Literature Review

1 First suggestion: (Narayanan, 1996) 2 First early studies: (Han and Kim, 2000) 3 Currently, over 160 papers

Quantum binary coding: about 130 papers Quantum real coding: 12 papers Applications: 79 Theoretical analysis: 5 papers Surveys: 2 papers

Robert Nowotniak

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SLIDE 13

Survey of Quantum-Inspired Evolutionary Algorithms

Literature Review

1 First suggestion: (Narayanan, 1996) 2 First early studies: (Han and Kim, 2000) 3 Currently, over 160 papers

Quantum binary coding: about 130 papers Quantum real coding: 12 papers Applications: 79 Theoretical analysis: 5 papers Surveys: 2 papers

Robert Nowotniak

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z, October 20, 2010 5 / 15

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SLIDE 14

Survey of Quantum-Inspired Evolutionary Algorithms

Literature Review

1 First suggestion: (Narayanan, 1996) 2 First early studies: (Han and Kim, 2000) 3 Currently, over 160 papers

Quantum binary coding: about 130 papers Quantum real coding: 12 papers Applications: 79 Theoretical analysis: 5 papers Surveys: 2 papers

Robert Nowotniak

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z, October 20, 2010 5 / 15

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Survey of Quantum-Inspired Evolutionary Algorithms

Annual Distribution of Papers on QIEAs

1 9 9 6 2 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 Year 5 10 15 20 25 30 35 40 Papers on QIEAs (Narayanan, 1996) (Han and Kim, 2000) Real-Coded QiEA

Robert Nowotniak

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z, October 20, 2010 6 / 15

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Survey of Quantum-Inspired Evolutionary Algorithms

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 Fitness evaluation

Robert Nowotniak

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z, October 20, 2010 7 / 15

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SLIDE 17

Survey of Quantum-Inspired Evolutionary Algorithms

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 Fitness evaluation

Robert Nowotniak

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z, October 20, 2010 7 / 15

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SLIDE 18

Survey of Quantum-Inspired Evolutionary Algorithms

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 Fitness evaluation

Robert Nowotniak

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z, October 20, 2010 7 / 15

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SLIDE 19

Survey of Quantum-Inspired Evolutionary Algorithms

Qubits and Binary Quantum Genes

Robert Nowotniak

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z, October 20, 2010 8 / 15

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Survey of Quantum-Inspired Evolutionary Algorithms

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

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Survey of Quantum-Inspired Evolutionary Algorithms

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

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z, October 20, 2010 8 / 15

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SLIDE 22

Survey of Quantum-Inspired Evolutionary Algorithms

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

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z, October 20, 2010 8 / 15

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SLIDE 23

Survey of Quantum-Inspired Evolutionary Algorithms

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

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z, October 20, 2010 8 / 15

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Survey of 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

                

population — chromosome — binary gene

Robert Nowotniak

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z, October 20, 2010 9 / 15

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Survey of Quantum-Inspired Evolutionary Algorithms

Situation for Simple Genetic Algorithm

0 1 0 1 1 1 1 0 1 0 1 0 1 0 0 0 1 0 0 1 1 1 0 1 0 1 1 0 1 1 1 0 0 1 1

                

population — chromosome — binary gene

Robert Nowotniak

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z, October 20, 2010 9 / 15

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SLIDE 26

Survey of 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

                

population — chromosome — binary gene

Robert Nowotniak

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z, October 20, 2010 9 / 15

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Survey of Quantum-Inspired Evolutionary Algorithms

Illustration of Quantum Population

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

quantum population — quantum chromosome — quantum gene

Robert Nowotniak

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z, October 20, 2010 10 / 15

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SLIDE 28

Survey of Quantum-Inspired Evolutionary Algorithms

Illustration of Quantum Population

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

quantum population — quantum chromosome — quantum gene

Robert Nowotniak

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z, October 20, 2010 10 / 15

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SLIDE 29

Survey of Quantum-Inspired Evolutionary Algorithms

Illustration of Quantum Population

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

quantum population — quantum chromosome — quantum gene

Robert Nowotniak

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z, October 20, 2010 10 / 15

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Survey of Quantum-Inspired Evolutionary Algorithms

Quantum-inspired elements bring a ”new dimension” into Evolutionary Algorithms. 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

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z, October 20, 2010 11 / 15

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SLIDE 31

Survey of Quantum-Inspired Evolutionary Algorithms

Quantum-inspired elements bring a ”new dimension” into Evolutionary Algorithms. 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

  • L´
  • d´

z, October 20, 2010 11 / 15

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SLIDE 32

Survey of Quantum-Inspired Evolutionary Algorithms

Quantum-inspired elements bring a ”new dimension” into Evolutionary Algorithms. 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

  • L´
  • d´

z, October 20, 2010 11 / 15

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SLIDE 33

Survey of Quantum-Inspired Evolutionary Algorithms

Quantum-inspired elements bring a ”new dimension” into Evolutionary Algorithms. 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

  • L´
  • d´

z, October 20, 2010 11 / 15

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SLIDE 34

Survey of Quantum-Inspired Evolutionary Algorithms

My Contributions

1 Convergence analysis of Quantum-Inspired Evolutionary

Algorithms based on Banach’s fixed point theorem

2 Building blocks propagation analysis

in Quantum-Inspired Genetic Algorithms[1]

3 Tuning QIEAs: meta-optimization[2]

1Nowotniak, R., Kucharski, J.: Building Blocks Propagation in Quantum-Inspired

Genetic Algorithm, Scientific Buletin of AGH. Automatics. 2011, in press

2Nowotniak, R., Kucharski, J.: Meta-optimization of Quantum-Inspired

Evolutionary Algorithm, will be presented on the incoming XVII International Conference on Information Technology Systems, 2010

Robert Nowotniak

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z, October 20, 2010 12 / 15

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SLIDE 35

Survey of Quantum-Inspired Evolutionary Algorithms

My Contributions

1 Convergence analysis of Quantum-Inspired Evolutionary

Algorithms based on Banach’s fixed point theorem

2 Building blocks propagation analysis

in Quantum-Inspired Genetic Algorithms[1]

3 Tuning QIEAs: meta-optimization[2]

1Nowotniak, R., Kucharski, J.: Building Blocks Propagation in Quantum-Inspired

Genetic Algorithm, Scientific Buletin of AGH. Automatics. 2011, in press

2Nowotniak, R., Kucharski, J.: Meta-optimization of Quantum-Inspired

Evolutionary Algorithm, will be presented on the incoming XVII International Conference on Information Technology Systems, 2010

Robert Nowotniak

  • L´
  • d´

z, October 20, 2010 12 / 15

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SLIDE 36

Survey of Quantum-Inspired Evolutionary Algorithms

My Contributions

1 Convergence analysis of Quantum-Inspired Evolutionary

Algorithms based on Banach’s fixed point theorem

2 Building blocks propagation analysis

in Quantum-Inspired Genetic Algorithms[1]

3 Tuning QIEAs: meta-optimization[2]

1Nowotniak, R., Kucharski, J.: Building Blocks Propagation in Quantum-Inspired

Genetic Algorithm, Scientific Buletin of AGH. Automatics. 2011, in press

2Nowotniak, R., Kucharski, J.: Meta-optimization of Quantum-Inspired

Evolutionary Algorithm, will be presented on the incoming XVII International Conference on Information Technology Systems, 2010

Robert Nowotniak

  • L´
  • d´

z, October 20, 2010 12 / 15

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SLIDE 37

Survey of Quantum-Inspired Evolutionary Algorithms

My Contributions

1 Convergence analysis of Quantum-Inspired Evolutionary

Algorithms based on Banach’s fixed point theorem

2 Building blocks propagation analysis

in Quantum-Inspired Genetic Algorithms[1]

3 Tuning QIEAs: meta-optimization[2]

1Nowotniak, R., Kucharski, J.: Building Blocks Propagation in Quantum-Inspired

Genetic Algorithm, Scientific Buletin of AGH. Automatics. 2011, in press

2Nowotniak, R., Kucharski, J.: Meta-optimization of Quantum-Inspired

Evolutionary Algorithm, will be presented on the incoming XVII International Conference on Information Technology Systems, 2010

Robert Nowotniak

  • L´
  • d´

z, October 20, 2010 12 / 15

slide-38
SLIDE 38

Survey of Quantum-Inspired Evolutionary Algorithms

My Contributions

1 Convergence analysis of Quantum-Inspired Evolutionary

Algorithms based on Banach’s fixed point theorem

2 Building blocks propagation analysis

in Quantum-Inspired Genetic Algorithms[1]

3 Tuning QIEAs: meta-optimization[2]

1Nowotniak, R., Kucharski, J.: Building Blocks Propagation in Quantum-Inspired

Genetic Algorithm, Scientific Buletin of AGH. Automatics. 2011, in press

2Nowotniak, R., Kucharski, J.: Meta-optimization of Quantum-Inspired

Evolutionary Algorithm, will be presented on the incoming XVII International Conference on Information Technology Systems, 2010

Robert Nowotniak

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  • d´

z, October 20, 2010 12 / 15

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SLIDE 39

Survey of Quantum-Inspired Evolutionary Algorithms

Example of Meta-Fitness Landscape

Real-coded QIEA with two parameters ξ, δ

Robert Nowotniak

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z, October 20, 2010 13 / 15

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SLIDE 40

Survey of Quantum-Inspired Evolutionary Algorithms

Example of Meta-Fitness Landscape

Real-coded QIEA with two parameters ξ, δ

0.0 0.2 0.4 0.6 0.8 1.0 crossover rate ξ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 contraction factor δ metafitness e f(ξ,δ) 27000 34500 42000 49500 57000 64500 72000 79500 87000 94500 Robert Nowotniak

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z, October 20, 2010 13 / 15

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SLIDE 41

Survey of Quantum-Inspired Evolutionary Algorithms

Example of Meta-Fitness Landscape

Real-coded QIEA with two parameters ξ, δ

0.0 0.2 0.4 0.6 0.8 1.0 crossover rate ξ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 contraction factor δ metafitness e f(ξ,δ) 27000 34500 42000 49500 57000 64500 72000 79500 87000 94500 Robert Nowotniak

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z, October 20, 2010 13 / 15

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SLIDE 42

Survey of Quantum-Inspired Evolutionary Algorithms

Example of Meta-Fitness Landscape

Real-coded QIEA with two parameters ξ, δ

0.0 0.2 0.4 0.6 0.8 1.0 crossover rate ξ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 contraction factor δ metafitness e f(ξ,δ) 27000 34500 42000 49500 57000 64500 72000 79500 87000 94500 Robert Nowotniak

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z, October 20, 2010 13 / 15

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SLIDE 43

Survey of Quantum-Inspired Evolutionary Algorithms

Example of Meta-Fitness Landscape

Real-coded QIEA with two parameters ξ, δ

0.0 0.2 0.4 0.6 0.8 1.0 crossover rate ξ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 contraction factor δ metafitness e f(ξ,δ) 27000 34500 42000 49500 57000 64500 72000 79500 87000 94500 Robert Nowotniak

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z, October 20, 2010 13 / 15

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SLIDE 44

Survey of Quantum-Inspired Evolutionary Algorithms

Example of Meta-Fitness Landscape

Real-coded QIEA with two parameters ξ, δ

0.0 0.2 0.4 0.6 0.8 1.0 crossover rate ξ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 contraction factor δ metafitness e f(ξ,δ) 27000 34500 42000 49500 57000 64500 72000 79500 87000 94500 Robert Nowotniak

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  • d´

z, October 20, 2010 13 / 15

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SLIDE 45

Survey of Quantum-Inspired Evolutionary Algorithms

Example of Meta-Fitness Landscape

Real-coded QIEA with two parameters ξ, δ

0.0 0.2 0.4 0.6 0.8 1.0 crossover rate ξ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 contraction factor δ metafitness e f(ξ,δ) 27000 34500 42000 49500 57000 64500 72000 79500 87000 94500 Robert Nowotniak

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  • d´

z, October 20, 2010 13 / 15

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SLIDE 46

Survey of Quantum-Inspired Evolutionary Algorithms

Example of Meta-Fitness Landscape

Real-coded QIEA with two parameters ξ, δ

0.0 0.2 0.4 0.6 0.8 1.0 crossover rate ξ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 contraction factor δ metafitness e f(ξ,δ) 27000 34500 42000 49500 57000 64500 72000 79500 87000 94500 Robert Nowotniak

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z, October 20, 2010 13 / 15

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SLIDE 47

Survey of Quantum-Inspired Evolutionary Algorithms

Experimentation Methodology

Robert Nowotniak

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z, October 20, 2010 14 / 15

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SLIDE 48

Survey of Quantum-Inspired Evolutionary Algorithms

Experimentation Methodology

1000 2000 3000 4000 5000 6000 Fitness function evaluation count 100 200 300 400 500 Objective function value

Particle Swarm Optimization algorithm Tuned QiEA(ξ,δ) algorithm

Robert Nowotniak

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z, October 20, 2010 14 / 15

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SLIDE 49

Survey of Quantum-Inspired Evolutionary Algorithms

Benchmarks and Selected Applications

1 Optimization test suites: ”De Jong’s suite”, CEC ’05 suite 2 Simultaneous Localization and Mapping (SLAM) problem for

mobile robots[1]

3 Feature selection problem for classifiers performing image

segmentation[2]

4 Inherently vague, imprecise, uncertain problems?

(evolving rough sets, fuzzy rules etc.)

1Je˙

zewski, S., Laski, M., Nowotniak, R.: Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, Scientific Buletin

  • f AGH. Automatics. 2011, submitted

2Jopek,

L., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Application of Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin of Academy of Science and Technology, Automatics, 2010 (in polish).

Robert Nowotniak

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z, October 20, 2010 15 / 15

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SLIDE 50

Survey of Quantum-Inspired Evolutionary Algorithms

Benchmarks and Selected Applications

1 Optimization test suites: ”De Jong’s suite”, CEC ’05 suite 2 Simultaneous Localization and Mapping (SLAM) problem for

mobile robots[1]

3 Feature selection problem for classifiers performing image

segmentation[2]

4 Inherently vague, imprecise, uncertain problems?

(evolving rough sets, fuzzy rules etc.)

1Je˙

zewski, S., Laski, M., Nowotniak, R.: Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, Scientific Buletin

  • f AGH. Automatics. 2011, submitted

2Jopek,

L., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Application of Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin of Academy of Science and Technology, Automatics, 2010 (in polish).

Robert Nowotniak

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  • d´

z, October 20, 2010 15 / 15

slide-51
SLIDE 51

Survey of Quantum-Inspired Evolutionary Algorithms

Benchmarks and Selected Applications

1 Optimization test suites: ”De Jong’s suite”, CEC ’05 suite 2 Simultaneous Localization and Mapping (SLAM) problem for

mobile robots[1]

3 Feature selection problem for classifiers performing image

segmentation[2]

4 Inherently vague, imprecise, uncertain problems?

(evolving rough sets, fuzzy rules etc.)

1Je˙

zewski, S., Laski, M., Nowotniak, R.: Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, Scientific Buletin

  • f AGH. Automatics. 2011, submitted

2Jopek,

L., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Application of Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin of Academy of Science and Technology, Automatics, 2010 (in polish).

Robert Nowotniak

  • L´
  • d´

z, October 20, 2010 15 / 15

slide-52
SLIDE 52

Survey of Quantum-Inspired Evolutionary Algorithms

Benchmarks and Selected Applications

1 Optimization test suites: ”De Jong’s suite”, CEC ’05 suite 2 Simultaneous Localization and Mapping (SLAM) problem for

mobile robots[1]

3 Feature selection problem for classifiers performing image

segmentation[2]

4 Inherently vague, imprecise, uncertain problems?

(evolving rough sets, fuzzy rules etc.)

1Je˙

zewski, S., Laski, M., Nowotniak, R.: Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, Scientific Buletin

  • f AGH. Automatics. 2011, submitted

2Jopek,

L., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Application of Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin of Academy of Science and Technology, Automatics, 2010 (in polish).

Robert Nowotniak

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  • d´

z, October 20, 2010 15 / 15

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SLIDE 53

Survey of Quantum-Inspired Evolutionary Algorithms

Thank you for your attention

Robert Nowotniak

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z, October 20, 2010