Evolutionary Computation
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1 Introduction Introduction Evolutionary Computation Lecture 2: Tutorial Claus Aranha Department of Computer Science July 24, 2013 Claus Aranha (Department of Computer Science) July 24, / 29
2 Introduction Introduction In the last Class... What is Evolutionary Computation? What is a Genetic Algorithm? What kind of problems can it solve? What are the components of a Genetic Agorithm? Claus Aranha (Department of Computer Science) July 24, / 29
3 Introduction Introduction Goal for this Class To be able to program a simple Genetic Algorithm Have an idea of how to write the basic functions of an genetic algorithm; Learn how to use a simple genetic algorithm library; Understand some considerations necessary when programming a genetic algorithm; Write two simple tutorial programs; Claus Aranha (Department of Computer Science) July 24, / 29
4 Introduction Introduction Class Outline Python Tutorial; Example 1: Max Ones problem (by hand); pyevolve Tutorial (Max Ones); Example 2: Nearest Color (pyevolve); Considerations when writing/running a GA; Example 3: Knapsack problem; Claus Aranha (Department of Computer Science) July 24, / 29
5 Python Tutorial Outline Mini Python Tutorial Crash course to Python scripting/programming Language; Minimum sintax necessary to program a Genetic Algorithm; If you don t have python installed in your computer, please install it now! Claus Aranha (Department of Computer Science) July 24, / 29
6 Python Tutorial Outline Some reference texts If you want to know more than the bare minimum I m covering here, check these links: handsonhtml/handson.html Claus Aranha (Department of Computer Science) July 24, / 29
7 Python Tutorial Outline Python Programming Environment If you don t have any experience programming in python, here is my suggestion for a programming environment. Three Python Windows Window 1: Python Interpreter open, for testing new things; Window 2: Text Editor, for writing your code; Window 3: Command Prompt, for running your code; Claus Aranha (Department of Computer Science) July 24, / 29
8 Python Tutorial Basic Structures Python Basic Concepts Weak typed language; Object Oriented; Interpreted; (bonus) how to import libraries; (bonus) how to write comments; Claus Aranha (Department of Computer Science) July 24, / 29
9 Python Tutorial Basic Structures Python Lists Basic Operations Creating lists; Appending, adding; List Multiplication; Slicing and Copying List[x:y]; copying and linking; Sorting List.sort() function; cmp(x,y) function; Claus Aranha (Department of Computer Science) July 24, / 29
10 Python Tutorial Basic Structures Control Loops Identation in python; for and lists; Claus Aranha (Department of Computer Science) July 24, / 29
11 Python Tutorial Basic Structures Defining Functions declaring functions; function scope; optional parameters; Claus Aranha (Department of Computer Science) July 24, / 29
12 Python Tutorial Basic Structures Using Objects declaring objects, attributes and methods; the self issue; dynamic attributes for instances; Claus Aranha (Department of Computer Science) July 24, / 29
13 Example 1 GA Review GA Framework Review The GA Framework What objects and functions do we need to create to have a GA? Claus Aranha (Department of Computer Science) July 24, / 29
14 Example 1 GA Review How to Implement our Own GA Representation (Genome); Mutation and Crossover; Evaluation; Population and Selection; Generation Call; Evolutionary Control;... better to explain each while programming. Claus Aranha (Department of Computer Science) July 24, / 29
15 Example 1 Problem 1: Maximum 1 s GA Let s Program! This is the same sample problem we introduced in the previous class. Try to evolve a binary string with a maximum number of 1s. Claus Aranha (Department of Computer Science) July 24, / 29
16 Example 1 Let s Program! Problem 1: The Genome Representation: Binary String; Crossover and Mutation: 1-point crossover and flip mutation; Variables and Parameters; Comparator; Finishing Touches (Constructors, dumps, tostrings); Claus Aranha (Department of Computer Science) July 24, / 29
17 Example 1 Let s Program! Problem 1: The Population Genome List; Selection Operator; The Evaluation Function; RunOneGeneration; RunEvaluations; Keeping track of data; Variables and Parameters; Finishing Touches; Claus Aranha (Department of Computer Science) July 24, / 29
18 Example 1 Let s Program! Problem 1: Writing the program Claus Aranha (Department of Computer Science) July 24, / 29
19 Example 1 Let s Run! Analyzing Our Results Best/Average individuals per generation; Distribution of genotypes in the population; Convergence Rate; Robustness of the Results; Claus Aranha (Department of Computer Science) July 24, / 29
20 What is PyEvolve? PyEvo The Library pyevolve is a python library that implements many useful functions for evolutionary algorithms; Why do we want to use a library? Claus Aranha (Department of Computer Science) July 24, / 29
21 PyEvolve Tutorial PyEvo The Library Claus Aranha (Department of Computer Science) July 24, / 29
22 Rewriting Problem 1 PyEvo Programming Example Claus Aranha (Department of Computer Science) July 24, / 29
23 PyEvo Programming Example Analyzing our Results with PyEvolve Claus Aranha (Department of Computer Science) July 24, / 29
24 Problem 2 This one is on you! Problem 2: Evolving a Camouflage Claus Aranha (Department of Computer Science) July 24, / 29
25 GA Considerations Closing Remarks Representation and Fitness issues Does the order of the bits matter in the Camouflage example? Claus Aranha (Department of Computer Science) July 24, / 29
26 GA Considerations Closing Remarks Analyzing an experimental Run What do we want to know? Is the optimum reached? Convergence speed; Diversity; Robustness of the results; Hacking around; Claus Aranha (Department of Computer Science) July 24, / 29
27 Problem 3 Programming Assignment Programming Assignment Write a Genetic Algorithm to solve the following, modified version of the Knapsack Problem: The Menu Problem There is a menu with many items. Each item has a money cost (price) and a time cost (delay). You have a fixed amount of money (resource). Find a combination of items from the menu where the price fits your money as closely as possible. (For example, if you have 1500 yens, you have to find a menu combination that gets as close to 1500 yens as possible, without going over). If two combinations have the same price, you want to choose the combination with the lowest time. Claus Aranha (Department of Computer Science) July 24, / 29
28 Problem 3 Programming Assignment Programming Assignment Simple and Advanced versions Simple version: in the solution, each item in the menu can only be selected once. Advanced version: in the solution, each item in the menu can be used as many times as necessary. Grading: Besides writing a GA program that solves this problem, follow the following points: Write a clear, well commented program (specially if you use GA libraries); Write your results, with commentary about how you choose your operators, and how you achieved those results (what worked, what didn t work, etc). Claus Aranha (Department of Computer Science) July 24, / 29
29 Problem 3 Programming Assignment Programming Assignment Data Set The data will be in a file, with the following format. A few data sets for testing will be available in the course webpage. Feel free to make your own! <Total money you own - integer> <item 1 name>,<item 1 price>,<item 1 time> <item 2 name>,<item 2 price>,<item 2 time>... <item N name>,<item N price>,<item N time> <END OF FILE> Claus Aranha (Department of Computer Science) July 24, / 29
30 Class 3 Preview For the Next class: Specialized GA Issues regarding forecasting problems; Multi-Objective GA; Differential Evolution; Genetic Programming; Estimation Distribution Algorithm; Claus Aranha (Department of Computer Science) July 24, / 29
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