A review on genetic algorithm: past, present, and future

Area of Science:

• Computational Intelligence
• Evolutionary Computation
• Artificial Intelligence

Background:

• Genetic algorithms (GAs) are powerful optimization and search techniques inspired by natural evolution.
• Recent advancements have expanded their applicability across diverse scientific domains.
• A structured overview is needed to guide new researchers in this rapidly evolving field.

Purpose of the Study:

• To review and analyze recent significant advances in genetic algorithms.
• To provide a comprehensive vision of GAs for researchers and graduate students.
• To highlight key algorithms, operators, and application areas.

Main Methods:

• Selection of prominent genetic algorithms based on research community interest.
• Analysis of well-known GA implementations, including their advantages and disadvantages.
• Discussion of genetic operators, their functionalities, and applications.
• Exploration of various research domains utilizing genetic algorithms.

Main Results:

• Identification of key genetic algorithms and their practical implementations.
• Detailed explanation of genetic operators and their strategic use.
• Overview of the diverse application landscape of genetic algorithms.
• Summary of the strengths and weaknesses of different GA approaches.

Conclusions:

• The review offers a structured perspective on genetic algorithms, beneficial for both novice and experienced researchers.
• Future research directions include advancements in genetic operators, fitness function design, and hybrid algorithmic approaches.
• This work serves as a valuable resource for academic research and graduate-level teaching in evolutionary computation.