A Brief Review on Binary-Coded Genetic Algorithm for Optimization

This research paper presents a comprehensive review of one of an Optimization technique in Operations Research (OR) known as "Binary-Coded Genetic Algorithm". This paper emphasizes their importance, past and recent developments, applications, challenges encountered, and prospective future directions. The paper starts with introduction of Genetic Algorithm by outlining its definition, scope, and application. Subsequently, it investigates into specialized techniques like Binary-Coded Genetic Algorithms and its historical evolution. This paper consist of the four major function (Rosenbrock, Himmelblau, Rastrigin and Ackley) to understand the selection, crossover, mutation operators and survivor strategies and visualize their behaviour graphically in approaching towards the optimal solution for a given problem. Recent innovations in optimization techniques including hybrid approaches, multi-objective optimization strategies for big data scenarios alongside integration with machine learning and artificial intelligence are scrutinized as well. Additionally, this paper addresses the problems faced within optimization research while identifying emerging trends that may shape future endeavours in the field.