A brief introduction to genetic algorithms Chapter 1: Hello World!  See the sample. 

• Guess a password given the number of correct letters in the guess. Build a mutation engine.  

Chapter 2: One Max Problem   See the sample. 

• Produce an array of bits where all are 1s.  Expands the engine to work with any type of gene.

Chapter 3: Sorted Numbers 

• Produce a sorted integer array. Demonstrates handling multiple fitness goals and constraints between genes.

Chapter 4: The 8 Queens Puzzle

• Find safe Queen positions on an 8x8 board and then expand to NxN. Demonstrates the difference between phenotype and genotype.

Chapter 5: Graph Coloring 

• Color a map of the United States using only 4 colors. Introduces standard data sets and working with files.  Also introduces using rules to work with gene constraints.

Chapter 6: Card Problem

• More gene constraints. Introduces custom mutation, memetic algorithms, and the sum-of-difference technique.  Also demonstrates a chromosome where the way a gene is used depends on its position in the gene array.

Chapter 7: Knights Problem

• Find the minimum number of knights required to attack all positions on a board. Introduces custom genes and gene-array creation. Also demonstrates local minimums and maximums.

Chapter 8: Magic Squares

• Find squares where all the rows, columns and both diagonals of an NxN matrix have the same sum. Introduces simulated annealing.

Chapter 9: Knapsack Problem

• Optimize the content of a container for one or more variables. Introduces branch and bound and variable length chromosomes.

Chapter 10: Solving Linear Equations

• Find the solutions to linear equations with 2, 3 and 4 unknowns.  Branch and bound variation.  Reinforces genotype flexibility.

Chapter 11: Generating Sudoku

• A guided exercise in generating Sudoku puzzles.

Chapter 12: Traveling Salesman Problem (TSP)

• Find the optimal route to visit cities. Introduces crossover and a pool of parents.

Chapter 13: Approximating Pi

• Find the two 10-bit numbers whose dividend is closest to Pi. Introduces using one genetic algorithm to tune another.

Chapter 14: Equation Generation

• Find the shortest equation that produces a specific result using addition, subtraction, multiplication, &c. Introduces symbolic genetic programming.

Chapter 15: The Lawnmower Problem

• Generate a series of instructions that cause a lawnmower to cut a field of grass. Genetic programming with control structures, objects and automatically defined functions (ADFs).

Chapter 16: Logic Circuits

• Build circuits that behave like basic logic gates, gate combinations and finally a 2-bit adder using tree nodes and hill climbing.

Chapter 17: Regular Expressions

• Find regular expressions that match wanted strings. Introduces chromosome repair and growth control.

Chapter 18: Tic-tac-toe

• Create rules for playing the game without losing. Introduces tournament selection.