Priya Sharma
Keywords:
Evolutionary Algorithms (EAs), Nature-Inspired Algorithms, Genetic Programming (GP),Abstract
Evolutionary algorithms (EAs) have emerged as powerful optimization techniques in engineering problem-solving, offering robust solutions for complex, nonlinear, and multi-objective problems. These nature-inspired algorithms, including Genetic Algorithms (GAs), Evolution Strategies (ES), Differential Evolution (DE), and Genetic Programming (GP), mimic biological evolution to iteratively improve candidate solutions through selection, crossover, and mutation operations. Due to their adaptability and global search capabilities, EAs have been extensively applied in various engineering domains, such as structural optimization, mechanical design, robotics, and industrial automation.
This review provides a comprehensive overview of the fundamental principles of EAs, their key variations, and their role in solving real-world engineering challenges. The study highlights the strengths and limitations of different evolutionary techniques and their performance in handling constrained, dynamic, and multi-objective optimization problems. Furthermore, the integration of evolutionary algorithms with machine learning, swarm intelligence, and metaheuristic hybridization is discussed, demonstrating their enhanced efficiency in tackling complex engineering tasks.
The article also explores recent advancements in evolutionary computation, including hybridization with artificial intelligence (AI), quantum-inspired evolutionary computing, and adaptive parameter control. These emerging trends aim to improve convergence speed, solution accuracy, and computational efficiency. Future research directions focus on developing more intelligent, scalable, and domain-specific evolutionary techniques that can address the increasing complexity of modern engineering problems.
By summarizing the evolution, applications, and future scope of EAs, this review provides valuable insights into their continued relevance and potential impact on engineering optimization and automation.
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