Genetic Algorithms Quiz

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Questions and Answers

What is a genetic algorithm and what is it inspired by?

A genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection.

What is the purpose of genetic algorithms?

The purpose of genetic algorithms is to generate high-quality solutions to optimization and search problems.

What are some examples of applications that use genetic algorithms?

Some examples of GA applications include optimizing decision trees, solving sudoku puzzles, hyperparameter optimization, and causal inference.

How are candidate solutions represented in a genetic algorithm?

Candidate solutions are traditionally represented as strings of 0s and 1s in binary encoding, but other encodings are also possible. Signup and view all the answers

What is the main goal of evolving a population of candidate solutions in a genetic algorithm?

The main goal is to evolve the population toward better solutions to an optimization problem. Signup and view all the answers

जेनेटिक एल्गोरिदम क्या है?

यह एक आपूर्ति और खोज समस्याओं के लिए उच्च गुणवत्ता की समाधान उत्पन्न करने के लिए प्रयोग किया जाने वाला एक तकनीक है (D) Signup and view all the answers

जेनेटिक एल्गोरिदम के लिए कौन से बायोलॉजिकल ऑपरेटर्स का उपयोग किया जाता है?

म्यूटेशन, क्रॉसओवर और सिलेक्शन (D) Signup and view all the answers

जेनेटिक एल्गोरिदम का उपयोग किसलिए किया जाता है?

ऑप्टिमाइजेशन और खोज समस्याओं के उच्च गुणवत्ता समाधान के लिए (C) Signup and view all the answers

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Study Notes

Genetic Algorithms

Genetic algorithms (GA) are a type of metaheuristic inspired by natural selection, belonging to the class of evolutionary algorithms (EA).
GA's are used to generate high-quality solutions to optimization and search problems.

Key Applications

Optimizing decision trees for better performance
Solving Sudoku puzzles
Hyperparameter optimization
Causal inference

Methodology

A population of candidate solutions (individuals, creatures, organisms, or phenotypes) is evolved towards better solutions for an optimization problem.
Each candidate solution has a set of properties (chromosomes or genotype) that can be mutated and altered.
Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also used.