Homework Help is Here – Start Your Trial Now!
Engineering
Computer Science
Know the principle of operation (algorithm) of the genetic algorithm approach ([2]: p. 99)

Know the principle of operation (algorithm) of the genetic algorithm approach ([2]: p. 99)

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
See similar textbooks
icon
Related questions
Question

Know the principle of operation (algorithm) of the genetic algorithm approach ([2]: p. 99)

Evolution Inside Your Computer that you do not need to know how to solve a problem; you only need to know how to encode it in a way the genetic algorithm mechanism can utilize. Typically, the chromosomes are encoded as a series of binary bits. At the start of a run, you create a population of chromosomes, and each chromosome has its bits set at random. The length of the chromosome is usually fixed for the entire popula- tion. As an example, this is what a chromosome of length twenty may look like: 0101001010010100|||| The important thing is that each chromosome is encoded in such a way that the string of bits may be decoded to represent a solution to the problem at hand. It may be a very poor solution, or it may be a perfect solution, but every single chromosome represents a possible solution (more on the encoding in a moment). Usually the starting population is terrible, a little like the English cricket team or an American playing football (sorry, soccer). Anyway, like I said, an initial population of random bits is created (let's say one hundred for this example), and then you do this (don't worry about the italicized phrases. I'll be explaining each one in just a moment): Loop until a solution is found: 1. Test each chromosome to see how good it is at solving the problem and assign a fitness score accordingly. 2. Select two members from the current population. The probability of being selected is proportional to the chromosome's fitness-the higher the fitness, the better the probability of being selected. A common method for this is called Roulette wheel selection. 3. Dependent on the Crossover Rate, crossover the bits from each chosen chro- mosome at a randomly chosen point. 4. Step through the chosen chromosome's bits and flip dependent on the Mutation Rate. 5. Repeat steps 2, 3, and 4 until a new population of one hundred members has been created. End loop Each loop through the algorithm is called a generation (steps 1 through 5). I call the entire loop an epoch and will be referring to it as such in my text and code. What's Roulette Wheel Selection? Roulette wheel selection is a method of choosing members from the population of chromosomes in a way that is proportional to their fitness-for example, the fitter 99
Transcribed Image Text:Evolution Inside Your Computer that you do not need to know how to solve a problem; you only need to know how to encode it in a way the genetic algorithm mechanism can utilize. Typically, the chromosomes are encoded as a series of binary bits. At the start of a run, you create a population of chromosomes, and each chromosome has its bits set at random. The length of the chromosome is usually fixed for the entire popula- tion. As an example, this is what a chromosome of length twenty may look like: 0101001010010100|||| The important thing is that each chromosome is encoded in such a way that the string of bits may be decoded to represent a solution to the problem at hand. It may be a very poor solution, or it may be a perfect solution, but every single chromosome represents a possible solution (more on the encoding in a moment). Usually the starting population is terrible, a little like the English cricket team or an American playing football (sorry, soccer). Anyway, like I said, an initial population of random bits is created (let's say one hundred for this example), and then you do this (don't worry about the italicized phrases. I'll be explaining each one in just a moment): Loop until a solution is found: 1. Test each chromosome to see how good it is at solving the problem and assign a fitness score accordingly. 2. Select two members from the current population. The probability of being selected is proportional to the chromosome's fitness-the higher the fitness, the better the probability of being selected. A common method for this is called Roulette wheel selection. 3. Dependent on the Crossover Rate, crossover the bits from each chosen chro- mosome at a randomly chosen point. 4. Step through the chosen chromosome's bits and flip dependent on the Mutation Rate. 5. Repeat steps 2, 3, and 4 until a new population of one hundred members has been created. End loop Each loop through the algorithm is called a generation (steps 1 through 5). I call the entire loop an epoch and will be referring to it as such in my text and code. What's Roulette Wheel Selection? Roulette wheel selection is a method of choosing members from the population of chromosomes in a way that is proportional to their fitness-for example, the fitter 99
Expert Solution
steps

Step by step

Solved in 3 steps with 4 images

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Knowledge Booster
Binary numbers
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.
Recommended textbooks for you
Database System Concepts
Database System Concepts
Computer Science
ISBN:
9780078022159
Author:
Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:
McGraw-Hill Education
Starting Out with Python (4th Edition)
Starting Out with Python (4th Edition)
Computer Science
ISBN:
9780134444321
Author:
Tony Gaddis
Publisher:
PEARSON
Digital Fundamentals (11th Edition)
Digital Fundamentals (11th Edition)
Computer Science
ISBN:
9780132737968
Author:
Thomas L. Floyd
Publisher:
PEARSON
C How to Program (8th Edition)
C How to Program (8th Edition)
Computer Science
ISBN:
9780133976892
Author:
Paul J. Deitel, Harvey Deitel
Publisher:
PEARSON
Database Systems: Design, Implementation, & Manag…
Database Systems: Design, Implementation, & Manag…
Computer Science
ISBN:
9781337627900
Author:
Carlos Coronel, Steven Morris
Publisher:
Cengage Learning
Programmable Logic Controllers
Programmable Logic Controllers
Computer Science
ISBN:
9780073373843
Author:
Frank D. Petruzella
Publisher:
McGraw-Hill Education
SEE MORE TEXTBOOKS