论文标题
通过$ε$ - 梅德收购功能加速加斯流程回归的进化算法
Accelerating the Evolutionary Algorithms by Gaussian Process Regression with $ε$-greedy acquisition function
论文作者
论文摘要
在本文中，我们提出了一种新的方法来估计精英人士加速优化的收敛性。受贝叶斯优化算法（BOA）的启发，高斯过程回归（GPR）可用于基于每一代优化的原始问题的适应性景观。简单但有效的$ε$ - 果岭采集功能用于在替代模型中找到有希望的解决方案。接近最佳原理（POP）指出，良好的解决方案具有相似的结构，并且在精英个人周围存在更好的解决方案的可能性很高。基于这一假设，在每一代优化中，我们用精英个人替换了进化算法（EAS）中最差的个体，以参与进化过程。为了说明我们的提议的可伸缩性，我们将建议与遗传算法（GA），差异进化（DE）和CMA-ES相结合。 CEC2013基准功能的实验结果表明，我们的建议具有广泛的前景，可以估算精英个人并加速优化的收敛性。
In this paper, we propose a novel method to estimate the elite individual to accelerate the convergence of optimization. Inspired by the Bayesian Optimization Algorithm (BOA), the Gaussian Process Regression (GPR) is applied to approximate the fitness landscape of original problems based on every generation of optimization. And simple but efficient $ε$-greedy acquisition function is employed to find a promising solution in the surrogate model. Proximity Optimal Principle (POP) states that well-performed solutions have a similar structure, and there is a high probability of better solutions existing around the elite individual. Based on this hypothesis, in each generation of optimization, we replace the worst individual in Evolutionary Algorithms (EAs) with the elite individual to participate in the evolution process. To illustrate the scalability of our proposal, we combine our proposal with the Genetic Algorithm (GA), Differential Evolution (DE), and CMA-ES. Experimental results in CEC2013 benchmark functions show our proposal has a broad prospect to estimate the elite individual and accelerate the convergence of optimization.
