可逆线性变换差分进化算法
Differential Evolution with Reversible Linear Transformations
摘要：
差分进化算法是一种著名的进化算法。类似于其他EA变种，它可能会遭受人口少和松散的多样性太快。本文提出了一种新的方法来缓解这个问题：我们提出了通过利用可逆线性变换应用到一个三元组的解决方案，从人口产生新的候选解决方案。换句话说，通过使用新生成的个体来扩大群体，而不评估它们的适应度。我们评估我们的方法上的三个问题：（i）基准函数优化，（ii）发现的基因repressilator系统的参数值，（iii）学习神经网络。实验结果表明，所提出的方法优于香草DE和一个版本的DE应用差分变异三次在所有的测试床。
英文摘要：
Differential evolution (DE) is a well-known type of evolutionary algorithms (EA). Similarly to other EA variants it can suffer from small populations and loose diversity too quickly. This paper presents a new approach to mitigate this issue: We propose to generate new candidate solutions by utilizing reversible linear transformation applied to a triplet of solutions from the population. In other words, the population is enlarged by using newly generated individuals without evaluating their fitness. We assess our methods on three problems: (i) benchmark function optimization, (ii) discovering parameter values of the gene repressilator system, (iii) learning neural networks. The empirical results indicate that the proposed approach outperforms vanilla DE and a version of DE with applying differential mutation three times on all testbeds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
