Ready to take the Genetic Algorithm MCQ Quiz? A genetic algorithm is a way of solving some optimization problems that don’t matter if they are constrained or unconstrained. It is important for one to get a proper hold of this algorithm when it comes to data mining. Do you think you do? Take up this 'genetic algorithm MCQ quiz' and get the chance to test your understanding of the genetic algorithm. Good luck!
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) Blind search cannot result in optimal solutions whereas optimization methods do
Blind search represents a guided approach while optimization is unguided.
Blind search usually does not conclude in one step like some optimization methods.
Blind search is usually a more efficient problem solving approach than optimization
A solution that can only be determined by an exhaustive enumeration and testing of alternatives.
A solution found in the least possible time and using the least possible computing resources
A solution that is the best based on criteria defined in the design phase
A solution that requires an algorithm for determination.
When a solution that is "good enough" is fine and good heuristics are available
when there is enough time and computational power available
when the modeler requires a guided approach to problem solving
when there are an infinite number of solutions to be searched
Heuristics are used when the modeler requires a guided approach to problem solving.
Heuristics are used when a solution that is "good enough" is sought.
Heuristics are used when there is abundant time and computational power.
Heuristics are rules of good judgment.
simulation
human intuition
Optimization
genetic algorithms
Artificial intelligence area.
Optimization area.
Complete enumeration family of methods
Non-computer based (human) solutions area
Dynamic process control.
Pattern recognition with complex patterns.
Simulation of biological models.
Simple optimization with few variables.
simulation
optimization
human intuition
genetic algorithms
It can incorporate significant real-life complexity.
It always results in optimal solutions.
Simulation software requires special skills.
It solves problems in one pass with no iterations
Defining the problem
Constructing the simulation model
Testing and validating the model
Designing the experiment