Static Segregative Genetic Algorithm for Optimizing Variable Ordering of ROBDDs

O. Brudaru, Cristian Rotaru, I. Furdu
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引用次数: 3

Abstract

This paper presents a segregative genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The main components are a basic genetic algorithm and two feature functions used to measure the similarity between chromosomes. Many copies of the basic genetic algorithm explore in parallel subpopulations induced in the search space by clustering in the feature space. A communication protocol preserves the similarity within each subpopulation during the evolution process. An associative tabu search memory is used to avoid reexploration of the search space. Extensive experimental evaluation proves the efficiency and stability of the segregative approach, which systematically produces better results than the basic genetic algorithm. The efficiency of the distributed implementation in terms of resource usage and many aspects regarding the communication protocol between different components are thoroughly described. The experiments used classical benchmarks known as very difficult and show that the segregative variant is better than the monopopulation algorithm and the approach using the island model.
静态分离遗传算法优化robdd变量排序
提出了一种优化降阶二值决策图中变量阶数的分离遗传算法。其主要组成部分是一个基本的遗传算法和两个用于测量染色体之间相似性的特征函数。基本遗传算法的许多副本通过在特征空间中聚类在搜索空间中产生的并行亚种群中进行探索。在进化过程中,通信协议保持了每个亚种群之间的相似性。使用关联禁忌搜索内存来避免对搜索空间的重新探索。大量的实验评价证明了该方法的有效性和稳定性,系统地产生了比基本遗传算法更好的结果。详细描述了分布式实现在资源使用方面的效率以及不同组件之间通信协议的许多方面。实验采用了非常困难的经典基准,结果表明,分离变异算法优于单种群算法和使用孤岛模型的方法。
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