Gene Pool Optimal Mixing

Gene pool optimal mixing (GOM) focuses on improving the efficiency of evolutionary algorithms by intelligently managing the exploration and exploitation of a solution space. Current research centers on the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA), exploring its application in diverse fields like Bayesian network learning, symbolic regression, and real-world optimization problems such as cancer treatment planning, often leveraging parallel computing and gray-box optimization techniques to enhance performance. This work is significant because GOMEA's ability to efficiently handle complex problems with intricate dependencies offers substantial improvements in solving computationally challenging optimization tasks across various scientific and engineering domains.