Closed Lab 3 Functions

Description: Genetic algorithms (GAs) find approximate solutions to difficult problems by
employing the principles of Darwinian evolution. The algorithm maintains a population
of candidate solutions that are iteratively improved through a number of generations
using reproduction, mutation, and survival of the fittest. Reproduction takes the form
of crossover. In crossover, two individuals (parents) are recombined to create some
number of new individuals (children). Each child contains some of the genome of each
parent. For this assignment, you will implement several crossover functions.
The genomes for this assignment are binary strings with fixed length 64. (In general,
genomes can be of any size and contain bits, integers, real numbers, etc.) That is, each
individual has a list of 64 bits that encodes its solution to the problem.
Details: Details of the crossover operations appear below. Each will be implemented as a
Python function that returns two children. You should also write a main program that
calls the crossover functions to test them.
1. Uniform crossover: Much like the problem from Quiz 3, uniform crossover exchanges bits. This function will create a copy of each parent. child1 is a copy of
parent1 and child2 is a copy of parent2. Each bit in child1 is replaced with
the corresponding bit from parent2 with probability indpb. child2 is similarly
altered by parent1.
parameters:
• parent1: list of bits
• parent2: list of bits
• indpb: probability in [0.0, 1.0], default value = 0.2
returns:
• child1: list of bits, defined above
• child2: list of bits, defined above
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2. One-point crossover: In one-point crossover, a split point is selected in the
genome. Assume that position k is chosen (at random) as the split point. Then
child1 would inherit genes 0..k from parent1 and genes k+1..n-1 from parent2.
child2 is created from the symmetric case.
parameters:
• parent1: list of bits
• parent2: list of bits
returns:
• child1: list of bits, defined above
• child2: list of bits, defined above
3. Two-point crossover: In two-point crossover, two split points are selected in
the genome. Assume that positions j and k are chosen (at random) as the split
points. Then child1 would inherit genes 0..k and j+1..n-1 from parent1 and
genes k+1..j from parent2. In other words, the middle section of child1 comes
from parent2. child2 is formed by the symmetric case.
parameters:
• parent1: list of bits
• parent2: list of bits
returns:
• child1: list of bits, defined above
• child2: list of bits, defined above
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