A Comprehensive Survey on Evolutionary Techniques and Its Application - A Research Perspective

Evolutionary algorithms are powerful techniques inspired by the principles of natural selection and biological evolution. This paper provides a comprehensive review of evolutionary algorithms by examining their historical development, key variants, and research trends. We also explore prominent algorithms, including Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Programming (EP), Evolution Strategies (ES), Differential Evolution (DE), Coevolutionary Algorithms, Neuroevolution and Learning Classifier Systems (LCS),Memetic Algorithms (MA), Quantum-Inspired Evolutionary Algorithms (QEA), Adaptive Differential Evolution (ADE), Multi-Objective Evolutionary Algorithms (MOEAs) detailing their principles and implementation steps.Special attention is given to key evolutionary operators such as selection, recombination (crossover), and mutation, which drive population diversity and optimization efficiency. Additionally, we consolidate widely-used evaluation metrics, facilitating performance assessment. The also paper discusses the applications of evolutionary algorithms in diverse fields such as image processing, machine learning, and bioinformatics while highlighting key challenges and future research directions. This survey drives as a valuable source for both novice and experienced researchers within the domain of evolutionary computation.