A genetic algorithm for energy minimization in bio-molecular systems

Energy minimization algorithms for bio-molecular systems are critical to applications such as the prediction of protein folding. Conventional energy minimization methods such as the steepest descent method and conjugate gradient method suffer from the drawback that they can only locate local energy minima that are extremely dependent on the initial parameter settings of the computation. Here we present an energy minimization algorithm based on genetic algorithms that largely overcomes this drawback of conventional methods because it provides a effective mechanism, through crossover and mutation, to explore new regions of the parameter space without being dependent on a single, preselected parameter setting. This allows the algorithm to cross local energy barriers not surmountable by conventional methods. The algorithm significantly increases the probability of reaching deeper energy minima and locating the global energy minimum. Tests show that the genetic algorithm based approach can ...

• Type: Conference
• Year: 2005
• Where: CEC
• Authors: Xiaochun Weng, Lutz Hamel, Lenore M. Martin, Joan Peckham
• Keywords: Artificial Intelligence, CEC 2005, Energy Minimization, Energy Minimization Algorithm, Genetic Algorithm