Evolutionary Computation Projects
This project investigates the effectiveness of Genetic Algorithms (GA) and Differential Evolution (DE) for solving constrained optimization problems using penalty functions. It compares their performance across three benchmark functions to find the minimum values. The study details the algorithms' implementation, parameters, and simulation results.
This project meticulously explores the application of Genetic Algorithms (GA) and Differential Evolution (DE) in tackling constrained optimization problems. The core objective is to assess their efficiency when augmented with penalty functions, aiming to identify the minimum values for a set of benchmark functions. The study provides an in-depth look at the implementation specifics of both algorithms, including chosen parameters such as population size (50) and maximum generations (1000). Furthermore, it details the specific operators used, such as convex crossover and dynamic mutation for GA, and binomial crossover for DE, along with the penalty function methodology. The comparative analysis of these evolutionary computation techniques offers insights into their respective strengths and weaknesses in solving complex optimization challenges.