基于贪婪多样性的多样性均衡遗传算法
Genetic and Memetic Algorithm with Diversity Equilibrium based on Greedy Diversification
摘要：
遗传算法种群多样性的缺乏可能导致遗传算子性能不佳，因为在探索和利用之间不存在平衡。在这些情况下，遗传算法呈现出快速和不合适的收敛。在本文中，我们开发了一种新的混合遗传算法，试图获得探索和开发之间的平衡。该算法利用贪心多样化算子解决了多样性问题。此外，该算法采用了父母和孩子之间的竞争，以开发高质量的访问解决方案。这些运营商是由一个简单的选择机制，旨在保护和利用人口的多样性。此外，我们将我们的建议扩展到模因算法领域，得到了一个改进的模型，在实践中取得了优异的成绩。实验研究表明，该方法的有效性，以及如何重要的是考虑到探索和开发的概念时，设计一个进化算法。
英文摘要：
The lack of diversity in a genetic algorithm's population may lead to a bad performance of the genetic operators since there is not an equilibrium between exploration and exploitation. In those cases, genetic algorithms present a fast and unsuitable convergence. In this paper we develop a novel hybrid genetic algorithm which attempts to obtain a balance between exploration and exploitation. It confronts the diversity problem using the named greedy diversification operator. Furthermore, the proposed algorithm applies a competition between parent and children so as to exploit the high quality visited solutions. These operators are complemented by a simple selection mechanism designed to preserve and take advantage of the population diversity. Additionally, we extend our proposal to the field of memetic algorithms, obtaining an improved model with outstanding results in practice. The experimental study shows the validity of the approach as well as how important is taking into account the exploration and exploitation concepts when designing an evolutionary algorithm.
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。
