# A Closer Look at AI: Genetic Algorithms

## Optimization: A Brief Overview

To figure out what genetic algorithms (GAs) are, start with optimization. Optimization is the process of improving things with each run. When we run the optimization process on several inputs, we get the output of our choice.

Additionally, selecting the best possible outcomes for the input to calculate the projected output values is optimization. The best possible values will undoubtedly vary depending on the problem. Still, this refers to minimizing or maximizing the aim functions by different versions of input variables in the mathematical realm.

The search space comprises all solutions and values that you can pass to the input. Search space is a point or set of points that yields the optimal solution we are looking for. The entire optimization goal is to find the point or set of points in that search space.

## What are Genetic Algorithms?

Genetic algorithms are search-based algorithms that draw their inspiration from nature itself. They are also a subset of computation called evolutionary computation.

John Holland and a group of his students and colleagues at the University of Michigan developed and introduced genetic algorithms. They were able to test various other optimization techniques with great success.

There is always a pool of the most likely solutions to any problem when using genetic algorithms. They recombine and mutate, producing new solutions. We continuously repeat the previous process for a variable number of generations. Then assign each one a fitness value. The fittest individuals get more opportunities to mix with and deduce fitter individuals. The process is utterly consistent with Darwin's theory of "Survival of the Fittest."

In this way, the fitter individuals continue to evolve generation after generation until the process is halted. The genetic algorithms are random, but they outperform the random local search because they also use historical information. Thus, the individual evolves over a specific set of generations.

## What is Their Role in AI?

To get to the heart of the matter, are genetic algorithms used in the field of artificial intelligence? This is a subjective question, but genetic algorithms are a subset of artificial intelligence according to how things are commonly defined. Let us go over a few points that will help us correctly mark these words:

● Problem-solving is a skill that is frequently associated with intelligence.
● Improving its previous experiences' learning
● Genetic algorithms are tools for solving computational problems (generation over generation, they evolve and learn).

Now that we have a better understanding of GAs let's discuss some of their advantages and disadvantages. Genetic Algorithms have a plethora of benefits, including:

● They are not dependent on derived data
● When compared to traditional brute-force search methods, they are faster and more efficient
● They have numerous parallel capabilities
● They optimize continuous and discrete functions as well as multi-objective problems

Some of the limitations of Genetic Algorithms include:

● They're not suitable for more straightforward problems with readily available derivative information
● Evaluation of fitness values over several generations is costly for a limited number of problems
● Misused, they may fail to converge to an optimal solution

## Summary

This article provides enough information for a newcomer to correlate things and grasp the basic concepts. This is only a drop in the bucket of what an individual hopes to learn about Genetic Algorithms.

At this point, it is recommended that you try to implement as many problems as you can to understand this algorithm's inner workings.

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