Memetic algorithms are techniques that orchestrate the interplay between population-based and trajectory-based algorithmic components. In particular, some memetic models can be regarded under this broad interpretation as a group of autonomous basic optimization algorithms that interact among them in a cooperative way in order to deal with a specific optimization problem, aiming to obtain better results than the algorithms that constitute it separately. Going one step beyond this traditional view of cooperative optimization algorithms, this work tackles deep meta-cooperation, namely the use of cooperative optimization algorithms in which some components can in turn be cooperative methods themselves, thus exhibiting a deep algorithmic architecture. The objective of this paper is to demonstrate that such models can be considered as an efficient alternative to other traditional forms of cooperative algorithms. To validate this claim, different structural parameters, such as the communication topology between the agents, or the parameter that influences the depth of the cooperative effort (the depth of meta-cooperation), have been analyzed. To do this, a comparison with the state-of-the-art cooperative methods to solve a specific combinatorial problem, the Tool Switching Problem, has been performed. Results show that deep models are effective to solve this problem, outperforming metaheuristics proposed in the literature.

翻译：模因算法是一种协调基于种群和基于轨迹的算法组件之间互动的技术。具体而言，在此广义解释下，某些模因模型可被视为一组自主的基本优化算法，它们以协作方式相互交互以处理特定优化问题，旨在获得比单独构成它的算法更好的结果。超越这种传统协作优化算法的视角，本研究探讨深度元协作，即采用协作优化算法，其中某些组件本身也可以是协作方法，从而展现出深层的算法架构。本文的目标是证明此类模型可作为其他传统协作算法形式的有效替代方案。为验证这一主张，我们分析了不同的结构参数，例如智能体间的通信拓扑，或影响协作努力深度（元协作深度）的参数。为此，我们与解决特定组合问题——刀具切换问题——的最先进协作方法进行了比较。结果表明，深度模型能有效解决该问题，其性能优于文献中提出的元启发式算法。