Genetic algorithms are a family of search algorithms inspired by the principles of evolution in nature. By imitating the process of natural selection and reproduction, genetic algorithms can produce high-quality solutions for various problems involving search, optimization, and learning. At the same time, their analogy to natural evolution allows genetic algorithms to overcome some of the hurdles that are encountered by traditional search and optimization algorithms, especially for problems with a large number of parameters and complex mathematical representations.

In the rest of this section, we will review the basic ideas of genetic algorithms, as well as their analogy to the evolutionary processes transpiring in nature.

The building-block hypothesis underlying genetic algorithms is that the optimal solution to the problem at hand is assembled of small building blocks, and as we bring more of these building blocks together, we get closer to this optimal solution.

Individuals in the population who contain some of the desired building blocks are identified by their superior scores. The repeated operations of selection and crossover result in the better individuals conveying these building blocks to the next generations, while possibly combining them with other successful building blocks. This creates genetic pressure, thus guiding the population toward having more and more individuals with the building blocks that form the optimal solution.

As a result, each generation is better than the previous one and contains more individuals that are closer to the optimal...

There are several important differences between genetic algorithms and traditional search and optimization algorithms, such as gradient-based algorithms.

The key characteristics of genetic algorithms distinguishing them from traditional algorithms are:

• Maintaining a population of solutions
• Using a genetic representation of the solutions
• Utilizing the outcome of a fitness function
• Exhibiting a probabilistic behavior

In the upcoming sections, we will describe these factors in greater detail.

The genetic search is conducted over a population of candidate solutions (individuals) rather than a single candidate. At any point in the search, the algorithm retains a set of...

The unique characteristics of genetic algorithms that we discussed in the previous sections provide several advantages over traditional search algorithms.

The main advantages of genetic algorithms are as follows:

• Global optimization capability
• Handling problems with a complex mathematical representation
• Handling problems that lack mathematical representation
• Resilience to noise
• Support for parallelism and distributed processing
• Suitability for continuous learning

We will cover each of these in the upcoming sections.

In many cases, optimization problems have local maxima and minima points; these represent solutions that are better than those around them, but not the best...

To get the most out of genetic algorithms, we need to be aware of their limitations and potential pitfalls.

The limitations of genetic algorithms are as follows:

• The need for special definitions
• The need for hyperparameter tuning
• Computationally-intensive operations
• The risk of premature convergence
• No guaranteed solution

We will cover each of these in the upcoming sections.

When applying genetic algorithms to a given problem, we need to create a suitable representation for them – define the fitness function and the chromosome structure, as well as the selection, crossover, and mutation operators that will work for this problem. This can often prove to be challenging...

Based on the material we covered in the previous sections, genetic algorithms are best suited for the following types of problems:

• Problems with complex mathematical representation: Since genetic algorithms only require the outcome of the fitness function, they can be used for problems with target functions that are hard or impossible to differentiate, problems with a large number of parameters, and problems with a mix of parameter types.
• Problems with no mathematical representation: Genetic algorithms don't require a mathematical representation of the problem as long as a score value can be obtained or a method is available to compare two solutions.
  
• Problems involving a noisy environment: Genetic algorithms are resilient to problems where data may not be consistent, such as data originating from sensor output or from human-based scoring.
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In this chapter, we started by introducing genetic algorithms, their analogy to Darwinian evolution, and their basic principles of operation, including the use of population, genotype, the fitness function, and the genetic operators of selection, crossover, and mutation.

Then, we covered the theory underlying genetic algorithms by going over the building-block hypothesis and the schema theorem and illustrating how genetic algorithms work by bringing together superior, small building blocks to create the best solutions.

Next, we went over the differences between genetic algorithms and traditional ones, such as maintaining a population of solutions and using a genetic representation of the solutions.

We continued by covering the strengths of genetic algorithms, including their capacity for global optimization, handling problems with complex or non-existent mathematical representations...

For more information on what we covered in this chapter, please refer to Introduction to genetic algorithms, from the book Hands-On Artificial Intelligence for IoT by Amita Kapoor, January 2019, available at https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781788836067.