The rapidly changing landscapes of modern optimization problems require
algorithms that can be adapted in real-time. This paper introduces an Adaptive
Metaheuristic Framework (AMF) designed for dynamic environments. It is capable
of intelligently adapting to changes in the problem parameters. The AMF
combines a dynamic representation of problems, a real-time sensing system, and
adaptive techniques to navigate continuously changing optimization
environments. Through a simulated dynamic optimization problem, the AMF's
capability is demonstrated to detect environmental changes and proactively
adjust its search strategy. This framework utilizes a differential evolution
algorithm that is improved with an adaptation module that adjusts solutions in
response to detected changes. The capability of the AMF to adjust is tested
through a series of iterations, demonstrating its resilience and robustness in
sustaining solution quality despite the problem's development. The
effectiveness of AMF is demonstrated through a series of simulations on a
dynamic optimization problem. Robustness and agility characterize the
algorithm's performance, as evidenced by the presented fitness evolution and
solution path visualizations. The findings show that AMF is a practical
solution to dynamic optimization and a major step forward in the creation of
algorithms that can handle the unpredictability of real-world problems.

该研究介绍了一个适应性元启发式框架（AMF），用于处理动态环境下的优化问题，它能智能地适应问题参数的变化，通过实时感知系统和适应性技术在连续变化的优化环境中导航。通过模拟的动态优化问题，AMF 展示了它能够检测环境变化并主动调整搜索策略的能力，通过应对检测到的变化调整解决方案的适应模块对改进的微分进化算法进行了验证。通过一系列迭代测试了 AMF 的调整能力，展示了其在问题发展过程中保持解决方案质量的韧性和稳健性。通过在动态优化问题上进行一系列模拟，验证了 AMF 的有效性。演示的适应性和敏捷性特征肯定了该算法在处理真实世界问题的不确定性方面的重要进展。

一个适应性元启发式框架用于复杂环境

An Adaptive Metaheuristic Framework for Changing Environments

We present novel techniques to accelerate the convergence of Deep Learning
algorithms by conducting low overhead removal of redundant neurons -- apoptosis
of neurons -- which do not contribute to model learning, during the training
phase itself. We provide in-depth theoretical underpinnings of our heuristics
(bounding accuracy loss and handling apoptosis of several neuron types), and
present the methods to conduct adaptive neuron apoptosis. Specifically, we are
able to improve the training time for several datasets by 2-3x, while reducing
the number of parameters by up to 30x (4-5x on average) on datasets such as
ImageNet classification. For the Higgs Boson dataset, our implementation
improves the accuracy (measured by Area Under Curve (AUC)) for classification
from 0.88/1 to 0.94/1, while reducing the number of parameters by 3x in
comparison to existing literature. The proposed methods achieve a 2.44x speedup
in comparison to the default (no apoptosis) algorithm.

该研究提出了一种通过在训练过程中低廉地去除不必要的神经元，即神经元凋亡的新技术，以加速深度学习算法的收敛，并成功应用于多个数据集中，在减少参数数量等方面得到了显著改善。