Genetic algorithms:

Question

Genetic algorithms:

BlackTom AI · Accepted Answer

Genetic algorithms use principles of natural selection and genetics—population of candidate solutions, fitness evaluation, selection, crossover (recombination), and mutation—to search for good solutions to optimization and search problems.

1. Relevant knowledge points and concepts
- Evolutionary computation: GAs belong to evolutionary algorithms inspired by biological evolution.
- Representation: candidate solutions (chromosomes) encode problem parameters.
- Fitness function: measures solution quality.
- Operators: selection favors fitter individuals; crossover recombines parents to create offspring; mutation introduces random variation.
- Application domain: optimization, combinatorial search, parameter tuning, machine learning model optimization.

2. Step-by-step reasoning process
- Start with an initial population of encoded candidate solutions.
- Evaluate each candidate with a fitness function to quantify how well it solves the problem.
- Select individuals probabilistically based on fitness (higher fitness → higher chance).
- Apply crossover to pairs of selected parents to produce offspring combining parts of parents’ encodings.
- Apply mutation to offspring to maintain diversity and explore new regions of the search space.
- Replace some or all of the population with offspring and repeat until a stopping criterion (e.g., sufficient fitness or iteration limit) is met.
Because these steps explicitly use mutation, crossover, and selection to evolve solutions, this directly matches the provided choice.

3. Intermediate steps / example
- Example: For a traveling salesman problem, encode tours as chromosomes, evaluate route length (fitness), select shorter routes, crossover segments between routes, and mutate by swapping cities; iterate to improve route length.

4. Why the correct answer is correct
The statement “develop solutions to particular problems using techniques such as mutation, crossover, and selection” precisely describes the mechanism and purpose of genetic algorithms—evolutionary search via those genetic operators.

5. Why other options are incorrect
- “Seek to emulate a human expert’s way of solving problems.” describes expert systems, not GAs.
- “Software programs that work in the background to carry out specific, repetitive tasks.” describes background services or bots, not GAs.
- “Are based on logic.” fits logic-based systems (e.g., rule-based), not evolutionary algorithms.
- “Represent knowledge as groups of characteristics.” refers to knowledge representation structures (frames/feature vectors), not the defining mechanism of GAs.

6. Conclusion
The correct answer is: develop solutions to particular problems using techniques such as mutation, crossover, and selection. This matches the provided answer.

Genetic algorithms: Single choice

Similar Questions

Which statement is NOT correct about Genetics Algorithm (GA)

DeepFace是遗传算法的一个例子。

DeepFace is an example of a genetic algorithm.

NSGA - II uses

The most popular multiobjective GA version is

In multiobjective genetic algorithms, diversity of final best solutions can be encouraged

Fill in the blanks with the appropriate choices. In Integer Mutation strategies, Random resetting is used for [Fill in the blank], (CARDINAL/ORDINAL) attributes and Creep mutation is used for [Fill in the blank], (CARDINAL/ORDINAL) attributes.

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler