Introduction to Evolutionary Algorithms

Abstract

Real-world has many optimization scenarios with multiple constraints and objective functions that are discontinuous, nonlinear, non-convex, and multi-modal in nature. Also, the optimization problems are multi-dimensional with mixed types of variables like integer, real, discrete, binary, and having a different range of values which demands normalization. Hence, the search space of the problem cannot be smooth. Evolutionary algorithms have started gaining attention and have been employed for computational processes to solve complex engineering problems. Because it has become an instrument for research scientists and engineers who need to apply the supremacy of the theory of evolution to shape any optimization-based research problems and articles. In this chapter, there is a comprehensive introduction to the optimization field with the state-of-the-art in evolutionary computation. Though many books have described such areas of optimization in any form as evolution strategies, genetic programming, genetic algorithms, and evolutionary programming, evolutionary algorithms, that is, evolutionary computation is remarkable for considering it to discuss in detail as a general class.