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Genetic Algorithms with Python

Get a hands-on introduction to machine learning with genetic algorithms using Python. Genetic algorithms are one of the tools you can use to apply machine learning to finding good, sometimes even optimal, solutions to problems that have billions of potential solutions. This book gives you experience making genetic algorithms work for you, using easy-to-follow example problems that you can fall back upon when learning to use other machine learning tools and techniques.

Each chapter begins with a project which you are encouraged to try to implement on your own before working through one possible implementation, and related pitfalls, with the author. This helps to build your skills at using genetic algorithms and prepares you to solve problems in your own field of expertise. The projects start with Hello World! then progress toward optimizing one genetic algorithm with another, and finally genetic programming. The following topics are introduced just-in-time: different ways to determine fitness, handling competing goals, phenotypes and genotypes, mutation options, memetic algorithms, local minimums and maximums, simulated annealing, branch and bound, variable length chromosomes, crossover, tuning genetic algorithms, symbolic genetic programming, automatically defined functions, hill climbing, chromosome repair, and tournament selection.

Python is used as the teaching language in this book because it is a high-level, low ceremony, and powerful language whose code can be easily understood even by entry-level programmers. Because Python is used for teaching, but is not being taught in this book, the use of Python-specific features that might make the code harder to follow for non-Python programmers has been minimized. This means that if you have experience with another programming language then you should have no difficulty using this book to learn about genetic algorithms while learning to at least read Python. Additionally, it should not be difficult for you to translate the working code used in this book to your favorite programming language on-the-fly, depending on the capabilities and support libraries available for your preferred language.

For a brief introduction to genetic algorithms and the writing style used in this book, use Amazon’s Look Inside feature, or use your Kindle Unlimited subscription to try it out, or download the sample chapters linked from the Github repository associated with this book. The source code is made available under the Apache License, Version 2.0.

 

Table of contents :

Genetic Algorithms with Python
Table of Contents
Preface
A brief introduction to genetic algorithms
Goal oriented problem solving
First project
Genetic programming with Python
About the author
About the text
Chapter 1. Hello World!
1.1. Guess my number
1.2. Guess the Password
1.3. First Program
1.3.1. Genes
1.3.2. Generate a guess
1.3.3. Fitness
1.3.4. Mutation
1.3.5. Display
1.3.6. Main
1.4. Extract a reusable engine
1.4.1. Generation and Mutation
1.4.2. get_best
1.4.3. Use the genetic module
1.4.4. Display
1.4.5. Fitness
1.4.6. Main
1.5. Use Python’s unittest framework
1.6. A longer password
1.6.1. Run
1.7. Introduce a Chromosome class
1.8. Benchmarking
1.9. Summary
1.10. Final Code
Chapter 2. One Max Problem
2.1. Solution
2.2. Make password code work with a list of genes
2.3. Change genetic to work with lists
2.4. Build the OneMax test class
2.5. Solution
2.5.1. Fitness
2.5.2. Display
2.5.3. Test
2.5.4. Run
2.6. Benchmarks
2.7. Aside
2.8. Summary
2.9. Final Code
Chapter 3. Sorted Numbers
3.1. Test class stub
3.2. Solution
3.2.1. Fitness
3.2.2. Display
3.2.3. sort_numbers
3.2.4. Run
3.3. Get 10 sorted digits
3.3.1. Run
3.4. Our first stall
3.5. Engineer a solution
3.6. Use a Fitness object
3.7. Use only > for fitness comparison
3.8. Run 2
3.9. Study the results
3.10. Engineer a solution
3.11. Run 3
3.12. Split get_best
3.13. Benchmarks
3.14. Summary
3.15. Final Code
Chapter 4. The 8 Queens Puzzle
4.1. Test class
4.2. Board
4.3. Display
4.4. Fitness
4.5. Test
4.6. Run
4.7. Benchmarks
4.8. Aside
4.9. Summary
4.10. Final Code
Chapter 5. Graph Coloring
5.1. Data
5.2. Reading the file
5.3. Rule
5.4. State adjacency Rules
5.5. Test class
5.6. Test
5.7. Genes
5.8. Display
5.9. Fitness
5.10. Run
5.11. Benchmarking
5.11.1. Convert the state file
5.11.2. Read the new file format
5.11.3. Extract parameters
5.11.4. Node indexes
5.11.5. Update the final output
5.11.6. Add the benchmark test
5.12. Benchmarks
5.13. Summary
5.14. Final Code
Chapter 6. Card Problem
6.1. Test class and genes
6.2. Fitness
6.3. Display
6.4. Test
6.5. Run
6.6. Study the result
6.7. Introducing custom mutation
6.8. Mutate
6.9. Run 2
6.10. Study the result
6.11. Engineer a solution
6.12. Run 3
6.13. Retrospective
6.14. Benchmarks
6.15. Summary
6.16. Final Code
Chapter 7. Knights Problem
7.1. Genes
7.2. Position
7.3. Attacks
7.4. Introducing custom_create
7.5. Create
7.6. Mutate
7.7. Display
7.8. Fitness
7.9. Test
7.10. Run
7.11. Test 8×8
7.11.1. Run
7.12. Try 10×10
7.12.1. Run
7.13. Performance
7.13.1. Study the result
7.13.2. Engineer a solution
7.13.3. Run
7.13.4. Choose wisely
7.13.5. Run 2
7.14. Retrospective
7.15. Benchmarks
7.16. Summary
7.17. Final Code
Chapter 8. Magic Squares
8.1. Test class
8.2. Test harness
8.3. Fitness
8.4. Display
8.5. Mutate
8.6. Run
8.7. Use sum of differences
8.8. Run 2
8.9. Fixing the local minimum / maximum issue
8.10. Set the max age
8.11. Run 3
8.12. Size-5 Magic Squares
8.12.1. Run
8.13. Size 10 Magic Squares
8.14. Retrospective
8.15. Benchmarks
8.16. Summary
8.17. Final Code
Chapter 9. Knapsack Problem
9.1. Resources
9.2. Test
9.3. ItemQuantity
9.4. Fitness
9.5. Max Quantity
9.6. Create
9.7. Mutate
9.8. Display
9.9. Test
9.10. Run
9.11. Use simulated annealing
9.12. Run 2
9.13. Solving a harder problem
9.13.1. File format
9.13.2. Parse the file
9.13.3. Test exnsd16
9.13.4. Run
9.14. Performance
9.14.1. Run
9.15. Retrospective
9.16. Benchmarks
9.17. Summary
9.18. Final Code
Chapter 10. Solving Linear Equations
10.1. Test class, test, and genes
10.2. Fitness
10.2.1. Fitness class
10.3. Optimal fitness
10.4. Display
10.5. Run
10.6. Use simulated annealing
10.7. Run 2
10.8. Fractions and 3 Unknowns
10.8.1. Refactoring
10.8.2. Test
10.8.3. Run
10.8.4. Use Fractions as the Genotype
10.8.5. Run 2
10.9. Finding 4 unknowns
10.9.1. Test
10.9.2. Run
10.10. Performance
10.10.1. Run
10.11. Benchmarks
10.12. Summary
10.13. Final Code
Chapter 11. Generating Sudoku
11.1. Test class and genes
11.2. Fitness
11.3. Display
11.4. Test
11.5. Run
11.6. Many options
11.7. Benchmarks
11.8. Summary
11.9. Final Code
Chapter 12. Traveling Salesman Problem
12.1. Test Data
12.2. Test and genes
12.3. Calculating distance
12.4. Fitness
12.5. Display
12.6. Mutate
12.7. Test
12.8. Run
12.9. A larger problem
12.10. Run
12.11. Introducing crossover
12.11.1. Support a pool of parents
12.11.2. Support crossover
12.11.3. Use crossover
12.12. Run
12.13. Retrospective
12.14. Updated benchmarks
12.15. Summary
12.16. Final Code
Chapter 13. Approximating Pi
13.1. Test and genes
13.2. Convert bits to an int
13.3. Fitness
13.4. Display
13.5. Best approximations for Pi
13.6. Optimal value
13.7. Run
13.8. Modify both parts
13.9. Run 2
13.10. Use simulated annealing
13.11. Run 3
13.12. Expanding the genotype
13.13. Pass the bit values
13.14. Change the bit values
13.15. Exercise
13.16. Optimizing the bit array
13.16.1. Support time constraints in genetic
13.16.2. Optimizer
13.16.3. Optimization Run
13.16.4. Verify the result
13.17. Summary
13.18. Final Code
Chapter 14. Equation Generation
14.1. Example
14.2. Evaluate
14.3. Test and genes
14.4. Create
14.5. Mutate
14.6. Display
14.7. Fitness
14.8. Test
14.9. Run
14.10. Support multiplication
14.10.1. Run
14.10.2. Extract solve
14.10.3. Test multiplication
14.10.4. Run 2
14.11. Refactoring
14.12. Supporting Exponents
14.13. Improve performance
14.13.1. Run
14.14. Benchmarks
14.15. Summary
14.16. Final Code
Chapter 15. The Lawnmower Problem
15.1. Part I – mow and turn
15.1.1. Virtual mower infrastructure
15.1.2. Test class
15.1.3. Create
15.1.4. Test
15.1.5. Evaluation
15.1.6. Fitness
15.1.7. Display
15.1.8. Mutate
15.1.9. Optimal fitness
15.1.10. Crossover
15.1.11. Test
15.1.12. Run
15.2. Part II – Jump
15.2.1. Implementation
15.2.2. Update the mower
15.2.3. Use lambdas to create genes
15.2.4. Run
15.3. Try a validating field
15.3.1. Run
15.3.2. Run 2
15.4. Part III – Repeat
15.4.1. Update Program
15.4.2. Test
15.4.3. Run
15.5. Optimizing for fuel efficiency
15.5.1. Run
15.6. Part IV – Automatically defined functions
15.6.1. The Func instruction
15.6.2. Run
15.7. Multiple ADFs
15.7.1. Run
15.8. Exercise
15.9. Summary
15.10. Final Code
Chapter 16. Logic Circuits
16.1. Circuit infrastructure
16.1.1. NOT and AND gates
16.1.2. Nodes to circuit
16.2. Generate OR
16.2.1. Fitness
16.2.2. Display
16.2.3. Create
16.2.4. Mutate
16.2.5. Create
16.2.6. Optimal OR circuit
16.2.7. Run
16.3. Generate XOR
16.3.1. Run
16.4. Performance improvement
16.5. Generate A XOR B XOR C
16.5.1. Hill climbing
16.5.2. Add hill climbing to the test harness
16.5.3. Run
16.6. Generate a 2-bit adder
16.6.1. Tests
16.6.2. Run
16.7. Retrospective
16.8. Summary
16.9. Final Code
Chapter 17. Regular Expressions
17.1. Test
17.2. Fitness
17.3. Display
17.4. Mutation
17.4.1. Mutation Operators
17.5. Test Harness
17.6. Run
17.7. Performance improvement
17.7.1. Regex repair
17.8. Groups
17.8.1. Repair
17.8.2. New test
17.9. Character-sets
17.9.1. Repair
17.9.2. New test
17.9.3. Supporting custom operators
17.10. Repetition
17.10.1. New test
17.11. State codes
17.11.1. Run 2
17.12. Exercise
17.13. Summary
17.14. Final Code
Chapter 18. Tic-tac-toe
18.1. Genes
18.2. Fitness
18.3. Mutation and Crossover
18.4. Results
18.5. Tournament selection
18.5.1. Implementation
18.6. Summary
18.7. Final Code
Afterward

Product information

Publisher‏:‎CreateSpace Independent Publishing Platform (April 29, 2016)
Language‏:‎English
Paperback‏:‎423 pages
ISBN-10‏:‎1540324001
ISBN-13‏:‎978-1540324009
Item Weight‏:‎1.66 pounds
Dimensions‏:‎7.44 x 0.96 x 9.69 inches
1540324001

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