Genetic Algorithm Parameters and Selection Methods Quiz

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Questions and Answers

What is the basis of Genetic Algorithm (GA) searching technique?

Deterministic algorithms

Mechanics of natural selection and genetics (correct)

First or second derivatives

Heuristic functions

In Genetic Algorithms (GAs), what is meant by 'survival of the fittest'?

All parents equally produce children

A random selection of parents produce children

The parents that are fittest are allowed to produce children (correct)

The parents that are weakest are allowed to reproduce

What does a Genetic Algorithm (GA) search for in terms of points?

The best point available

Randomly scattered points

A single point

A population of points (correct)

Which function does a Genetic Algorithm (GA) use directly to evaluate solutions?

Objective function value Signup and view all the answers

What type of rules does a Genetic Algorithm (GA) use for decision-making?

Stochastic rules based on probability or random numbers Signup and view all the answers

Why does a Genetic Algorithm (GA) work with encoding of parameter sets instead of the parameters themselves?

To better represent the solution space Signup and view all the answers

What is the purpose of mutation in genetic algorithms?

To introduce randomness and explore new solutions Signup and view all the answers

Which type of crossover involves swapping alternating segments of parents' genetic material?

Multi Point Crossover Signup and view all the answers

In Uniform Crossover, how are genes treated during the crossover operation?

Treated independently without segmenting Signup and view all the answers

What happens in Whole Arithmetic Recombination when the mixing ratio (α) is 0.5?

The children will be identical Signup and view all the answers

How does One Point Crossover differ from Multi Point Crossover?

One Point involves a single crossover point, Multi Point involves multiple crossover points Signup and view all the answers

What is the key difference between Crossover and Mutation in genetic algorithms?

Crossover introduces randomness, Mutation optimizes current solutions Signup and view all the answers

What is the main difference between Stochastic Universal Sampling (SUS) and Roulette Wheel Selection?

SUS has multiple fixed points while Roulette Wheel Selection has only one Signup and view all the answers

What is a key advantage of Stochastic Universal Sampling over Roulette Wheel Selection?

SUS allows highly fit individuals to be chosen at least once Signup and view all the answers

When is Rank Selection typically employed in genetic algorithms?

When selecting individuals with similar fitness values towards the end of the run Signup and view all the answers

What distinguishes Stochastic Universal Sampling from Tournament Selection?

Stochastic Universal Sampling can handle negative fitness values unlike Tournament Selection Signup and view all the answers

How does Tournament Selection encourage diversity in parent selection?

By having multiple rounds of selection from random subsets of the population Signup and view all the answers

What is the purpose of representing probabilities on a roulette wheel in Genetic Algorithms?

To visualize the selection process Signup and view all the answers

Why do fitter individuals have a higher chance of becoming parents in Fitness Proportionate Selection?

Because they have a higher fitness value Signup and view all the answers

Which selection method in Genetic Algorithms relies on dividing a circular wheel into pies proportional to fitness values?

Roulette Wheel Selection Signup and view all the answers

What happens in Genetic Algorithms when the fitness can take a negative value?

Fitness proportionate selection methods fail Signup and view all the answers

How does Roulette Wheel Selection determine the portion of the circle for each individual solution?

Proportional to fitness value Signup and view all the answers

What is the main distinction between Stochastic Universal Sampling (SUS) and Roulette Wheel Selection?

SUS guarantees an equal chance for all individuals Signup and view all the answers

What does the GA do in situations where there is a loss in the selection procedure towards fitter individuals?

Makes poor parent selections Signup and view all the answers

How are parents selected in the GA when the fitness value concept is removed?

Based on their ranks Signup and view all the answers

What does the higher rank of an individual in the GA population indicate?

Preference for selection as a parent Signup and view all the answers

In the given example, which chromosome has the highest fitness value?

P1 Signup and view all the answers

What is the probability of selection for Chromosome P6 in the Roulette Wheel Selection?

16.81% Signup and view all the answers

How are mating parents selected after spinning in the Roulette Wheel Selection?

Based on the spin outcome Signup and view all the answers

Study Notes

Genetic Algorithm Based Searching

Genetic Algorithm (GA) is a searching technique based on the mechanics of natural selection and natural genetics.
GA is based on the "survival of the fittest" theory, where the fittest parents are allowed to produce children (new states).
GA simulates the process of evolution, considering evolution as an optimization problem where every successor generation is better.

Differences Between GA and Other Searching Methods

GA works with encoding of parameter sets, not the parameters themselves.
GA searches for a population of points, not a single point.
GA uses objective function value (also called fitness function) directly, rather than any heuristic function or first or second derivatives.
GA uses stochastic rules (based on probability or random numbers) rather than deterministic algorithms.

Genetic Algorithm Phases

The algorithm begins with an initial population, which is a set of randomly selected states that are satisfactory to the problem.
The initial population may contain any sequence of states from the start city.

Genetic Algorithm Flow Chart

GA involves crossover and mutation operations to produce new offspring.
Crossover is used for exploration, while mutation is used for exploitation.

Crossover Operators

One-point crossover: a random crossover point is selected, and the tails of the two parents are swapped to get new offspring.
Multi-point crossover: alternating segments are swapped to get new offspring.
Uniform crossover: each gene is treated separately, and a coin is flipped to decide whether or not it will be included in the offspring.
Whole arithmetic recombination: a weighted average of the two parents is taken to produce new offspring.

GA Tuning Parameters

Population size: 50, 100
Crossover rate: 0.5 to 0.9
Mutation rate: 0.01
Fitness function

Selection Methods in GA

Roulette Wheel Selection: a probability of selection is calculated for each population, and a fixed point is chosen on the wheel circumference.
Fitness Proportionate Selection: every individual can become a parent with a probability proportional to its fitness value.
Stochastic Universal Sampling (SUS): similar to roulette wheel selection, but with multiple fixed points.
Tournament Selection: K individuals are selected at random, and the best is chosen to become a parent.
Rank Selection: individuals are ranked according to their fitness, and the selection of parents depends on the rank.

Example of GA

The initial population and fitness function values are given, and a mating pool is generated using roulette wheel and ranking selection methods.
Crossover operator is applied using single point crossover.

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Description

Test your knowledge on tuning parameters in genetic algorithms such as population size, crossover rate, and mutation rate. Learn about selection methods like Roulette Wheel Selection and how to compute probabilities for individual selection.