Genetic Algorithms

Genetic Algorithms.





1) Let us assume that we have a problem we want to solve in which we encode chromosomes with sequences of 8 binary bits. The fitness function values are calculated as the number of zeros (‘0’) in each chromosome. The current population consists of the following chromosomes 11001101, 11100110, 10101100, 00110111, 10100100, 10001101. The roulette wheel selection method is used. Construct this roulette (probabilities of selection for each individual).


2) Let us assume that we want to develop a genetic algorithm which solves the following problem:

Find 4 numbers between 0 and 255 whose binary representation includes an equal number of zeros and ones.

i) How you would encode the chromosomes and what fitness function would you define for this problem?

ii) Describe the selection, crossover and mutation steps involved in the first iteration of the algorithm using an initial population of 4 chromosomes.


3) Describe a problem from your work environment or your everyday environment, which you think that could be solved efficiently using genetic algorithms(max 2 paragraphs).


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Genetic Algorithms


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