Evolutionary Algorithms

The evolutionary algorithm is one of the various prevailing meta-heuristic algorithms. The evolutionary algorithm was found to be fundamental because it was developed in the past and it can be used for solving many optimization problems. To achieve efficacy in searches various parameters have been defined. Many of the rough guidelines related to empirical experience is present but the corresponding setting is complex to find. For solving these sorts of issues non-static parameter control can be utilized. Three kinds of non-static parameter controls are present which are classified by Back (1996). The modest variant is found to be dynamic parameter control. Only on the basis of certain schemes will the parameter be set. These schemes will mainly be influenced by quantity of generation. The scheme of control is considered significant in the case of adaptive parameter controls. The single encounter is taken as a function value. Some of the parameters that are found in the evolutionary algorithm such as selection, crossover, and mutation must be utilized for creating the parameter to be used in self-adaptive parameter control. The three above-mentioned variants are utilized now, but there is a theoretical model related to it. For the case of discrete objective function-related optimization, these variants are well suited. The continuous theoretical model was developed in evolution approach (Schwefel, 1995; Beyer, 1996; Rudolph. 1997b).

Applicability of Soft Computing Techniques in Software Engineering

The technique called soft computing has been utilized broadly recently in the solution of software engineering problems. Different soft computing techniques are applied in different ways within software business. Mainly, the applicability of techniques related to soft computing in the field of software business can be classified into following four classes:

  • • Neural Network Concepts Usage in Software Engineering.
  • • Fuzzy Logic Concepts Usage in Software Engineering.
  • • Genetic Algorithm Concepts Usage in Software Engineering.
  • • Machine Learning, Deep Learning, and SVM Concepts Usage in Software Engineering.
 
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