Reference no: EM132539782
COMP11071 Intelligent Systems - University of the West of Scotland
Evolutionary Computation
The application of Genetic Algorithms to optimisation problems is becoming increasingly more widespread. The following is a maximisation problem with three variables.
Max ,
where F is the objective function to be maximise; x, y and z are three integer variables which vary between 0 to 15.
By utilising a GA style optimisation process with:
• a binary representation for variables,
• population size set to six,
• selecting the best solution twice, worst solution once, and other three solutions randomly for reproduction
• the two-point crossover with cut points chosen by you and crossover probability (rate) set to 1
• a random mutation with small rate of your choice, and
• an initial population with the decimal values of variables for six solutions as below:
|
Initial Population: P0
|
|
x
|
y
|
z
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0
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10
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4
|
|
2
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1
|
5
|
|
10
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3
|
12
|
|
3
|
5
|
14
|
|
2
|
15
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4
|
|
1
|
5
|
2
|
demonstrate in paper the GA process for the next three generations showing the binary encoding/decoding, evaluation value (and relative evaluation value) for the members of populations in each generation, and GA operations. Also, identify the best solution found in each of the generations.