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‎LICENSE

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Apache License
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‎README.md

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# Genetic Algorithms with Python
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Source code from the book Genetic Algorithms with Python by Clinton Sheppard
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![alt text](http://www.cs.unm.edu/~sheppard/img/Genetic_Algorithms_with_Python_cover.jpg "Genetic Algorithms with Python")
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ISBN-10: 1540324001, ISBN-13: 978-1540324009
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Get it from:
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- https://www.amazon.com/Genetic-Algorithms-Python-Clinton-Sheppard/dp/1540324001/ (paperback)
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- https://www.amazon.com/dp/B01MYOWVJ2/ (Kindle)
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Table of Contents
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===
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A brief introduction to genetic algorithms
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Chapter 1: Hello World!
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- Guess a password given the number of correct letters in the guess. Build a mutation engine.
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Chapter 2: One Max Problem
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- Produce an array of bits where all are 1s. Expands the engine to work with any type of gene.
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Chapter 3: Sorted Numbers
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- Produce a sorted integer array. Demonstrates handling multiple fitness goals and constraints between genes.
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Chapter 4: The 8 Queens Puzzle
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- Find safe Queen positions on an 8x8 board and then expand to NxN. Demonstrates the difference between phenotype and genotype.
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Chapter 5: Graph Coloring
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- 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.
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Chapter 6: Card Problem
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- 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.
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Chapter 7: Knights Problem
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- 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.
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Chapter 8: Magic Squares
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- Find squares where all the rows, columns and both diagonals of an NxN matrix have the same sum. Introduces simulated annealing.
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Chapter 9: Knapsack Problem
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- Optimize the content of a container for one or more variables. Introduces branch and bound and variable length chromosomes.
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Chapter 10: Solving Linear Equations
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- Find the solutions to linear equations with 2, 3 and 4 unknowns. Branch and bound variation. Reinforces genotype flexibility.
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Chapter 11: Generating Sudoku
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- A guided exercise in generating Sudoku puzzles.
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Chapter 12: Traveling Salesman Problem (TSP)
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- Find the optimal route to visit cities. Introduces crossover and a pool of parents.
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Chapter 13: Approximating Pi
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- Find the two 10-bit numbers whose dividend is closest to Pi. Introduces using one genetic algorithm to tune another.
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Chapter 14: Equation Generation
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- Find the shortest equation that produces a specific result using addition, subtraction, multiplication, &c. Introduces symbolic genetic programming.
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Chapter 15: The Lawnmower Problem
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- 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).
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Chapter 16: Logic Circuits
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- Generate circuits that behave like basic gates, gate combinations and finally a 2-bit adder. Introduces tree nodes and hill climbing.
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Chapter 17: Regular Expressions
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- Find regular expressions that match wanted strings. Introduces chromosome repair and growth control.
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Chapter 18: Tic-tac-toe
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- Create rules for playing the game without losing. Introduces tournament selection.

‎ch01/genetic.py

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# File: genetic.py
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# from chapter 1 of _Genetic Algorithms with Python_
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#
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# Author: Clinton Sheppard <[email protected]>
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# Copyright (c) 2016 Clinton Sheppard
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#
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# Licensed under the Apache License, Version 2.0 (the "License").
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# You may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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# implied. See the License for the specific language governing
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# permissions and limitations under the License.
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import random
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import statistics
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import sys
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import time
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def _generate_parent(length, geneSet, get_fitness):
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genes = []
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while len(genes) < length:
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sampleSize = min(length - len(genes), len(geneSet))
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genes.extend(random.sample(geneSet, sampleSize))
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genes = ''.join(genes)
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fitness = get_fitness(genes)
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return Chromosome(genes, fitness)
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def _mutate(parent, geneSet, get_fitness):
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index = random.randrange(0, len(parent.Genes))
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childGenes = list(parent.Genes)
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newGene, alternate = random.sample(geneSet, 2)
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childGenes[index] = alternate if newGene == childGenes[index] else newGene
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genes = ''.join(childGenes)
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fitness = get_fitness(genes)
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return Chromosome(genes, fitness)
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def get_best(get_fitness, targetLen, optimalFitness, geneSet, display):
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random.seed()
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bestParent = _generate_parent(targetLen, geneSet, get_fitness)
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display(bestParent)
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if bestParent.Fitness >= optimalFitness:
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return bestParent
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while True:
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child = _mutate(bestParent, geneSet, get_fitness)
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if bestParent.Fitness >= child.Fitness:
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continue
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display(child)
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if child.Fitness >= optimalFitness:
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return child
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bestParent = child
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class Chromosome:
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Genes = None
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Fitness = None
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def __init__(self, genes, fitness):
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self.Genes = genes
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self.Fitness = fitness
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class Benchmark:
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@staticmethod
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def run(function):
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timings = []
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stdout = sys.stdout
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for i in range(100):
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sys.stdout = None
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startTime = time.time()
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function()
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seconds = time.time() - startTime
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sys.stdout = stdout
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timings.append(seconds)
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mean = statistics.mean(timings)
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if i < 10 or i % 10 == 9:
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print("{0} {1:3.2f} {2:3.2f}".format(
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1 + i, mean,
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statistics.stdev(timings, mean) if i > 1 else 0))

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