Health, 17.01.2021 14:00 linnybear300

A) Using the Genetic algorithm approach – reproduction crossover and mutation technique, determine the number of guesses that will enable you arrive at the following secret string of genes: 101010

Assume that four strings selected randomly as shown below have been initially presented to your opponent
A. 010100 = 1 digit correctly guessed
B. 111101 = 2 digit correctly guessed
C. 011011 = 3 digits correctly guessed
D. 101100 = 4 digits correctly guessed

(20 marks)

b) Genetic algorithms are said to be similar to a biological process.
Discuss. (5 marks)

Answers: 1

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Answer from: paulasuarez8276

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Christopher Scheidel

over 3 years ago

Great introduction for those new to GAs. I’ve been working with GAs for about 19 years now and another way that my business partner and I have represented them is as binary trees. With this concept you can think of mutation as “leaf replacement” or…...

2 replies

Jeff Flynt

over 3 years ago

Coming from a microbiology background I understand the biology portion, but can you provide a real world problem that this can be applied to? I see you mentioned search, but can you go into more details?

4 replies

Dan Lovy

about 3 years ago

Very nice article. I have just completed a GA project that applies this to neural simulation —

Eranga Heshan

over 3 years ago

Wow… Need to run the code see by myself O:)

1 reply

Anuradha Wickramarachchi

over 3 years ago

Excellent piece of work!!

1 reply

mem ento

about 1 year ago

Hello Ms Mallawaarachchi,

Very nice article !

Germán Meléndrez Carballo

over 3 years ago

I think that a very valuable resource/reference you can provide is the book: Introduction to evolutionary computing, 2nd edition, A. E. Eiben & J. E. Smith.

1 reply

Wenderson Júnio

about 3 years ago

Awesome ! Very well explained ! Thanks for sharing.

1 reply

Dip Patel

over 3 years ago

Great Post!!

Can you do more deeper into Evaluation Strategies in next post ?

1 reply

Arun Prasad T S V

over 3 years ago

Hello!

Please share the coding for creating cryptography (encryption,decryption) using genetic algorithm.

with regards,

T.S.V.ARUNPRASAD

1 reply

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Introduction to Genetic Algorithms — Including Example Code

Vijini Mallawaarachchi

Jul 8, 2017·4 min read

A genetic algorithm is a search heuristic that is inspired by Charles Darwin’s theory of natural evolution. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation.

Image for post

Notion of Natural Selection

The process of natural selection starts with the selection of fittest individuals from a population. They produce offspring which inherit the characteristics of the parents and will be added to the next generation. If parents have better fitness, their offspring will be better than parents and have a better chance at surviving. This process keeps on iterating and at the end, a generation with the fittest individuals will be found.

This notion can be applied for a search problem. We consider a set of solutions for a problem and select the set of best ones out of them.

Five phases are considered in a genetic algorithm.

Initial population

Fitness function

Selection

Crossover

Mutation

Initial Population

The process begins with a set of individuals which is called a Population. Each individual is a solution to the problem you want to solve.

An individual is characterized by a set of parameters (variables) known as Genes. Genes are joined into a string to form a Chromosome (solution).

In a genetic algorithm, the set of genes of an individual is represented using a string, in terms of an alphabet. Usually, binary values are used (string of 1s and 0s). We say that we encode the genes in a chromosome.

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Population, Chromosomes and Genes

Answer from: Quest

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Answer from: Quest

if this is true of false i think it would be true

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