Genetic Algorithms with Python

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 8x8
		7.11.1. Run
	7.12. Try 10x10
		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