An evolutionary algorithm (EA) is known as a subset of evolutionary
computation. It is inspired by natural evolution and applies natural phenomena to
search for the optimal solution. Its parallel search capability and randomized nature
enable it to be effective and unique in solving different real-world problems in
comparison to existing classical optimization algorithms. The evolutionary algorithm
applies biological techniques such as selection, reproduction, and mutation to solve
complex problems. It starts with a random population of candidate solutions and
applies biological techniques to every generation until feasible solutions are obtained.
The only fit solutionis intelligence (AI) simulation human intelligence in machines.
Machines are programmed enough to think like humans and imitate their actions. AI
based models are developed to provide new solutions to real-world problems. As realworld problems are very complex, the desired solutions for such problems are required
to be explored in complex, high-dimensional, and very large search spaces. In this
context, nature inspired and population based evolutionary techniques are the most
suitable approach to find the optimal solution. The nature-inspired evolutionary
techniques follow the natural phenomenon and these phenomenon helps to search for
the desired optimal solution when the direction of the search is allowed to survive and
continue to move in further generations to determine the optimal solution. Artificial not
known at the beginning. So, “Evolutionary Artificial Intelligence (EAI)” is the term
that presents the combination of human intelligence and natural phenomenon-based
solutions to real-world complex problems. This chapter covers an overview of
optimization techniques, artificial intelligence, and evolutionary computation in detail. A detailed discussion on evolutionary artificial intelligence, followed by applications of
evolutionary machine learning is also presented. After that, the significance of
evolutionary artificial intelligence in decision making has been discussed. Finally, the
conclusion has been given, which shows the summary of the chapter.
Keywords: Artificial intelligence, Evolutionary algorithms, Optimization techniques, Natural evolution.
