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首页> 外文会议>International Workshop on Foundations of Genetic Algorithms(FOGA 2005); 20050105-09; Aizu-Wakamatsu City(JP) >Running Time Analysis of a Multiobjective Evolutionary Algorithm on Simple and Hard Problems
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Running Time Analysis of a Multiobjective Evolutionary Algorithm on Simple and Hard Problems

机译：多目标进化算法在简单和困难问题上的运行时间分析

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摘要

In this paper, we suggest a multiobjective evolutionary algorithm based on a restricted mating pool (REMO) with a separate archive for storing the remaining population. Such archive based algorithms have been used for solving real-world applications, however, no theoretical results are available. In this paper, we present a rigorous running time complexity analysis for the algorithm on two simple discrete pseudo boolean functions and on the multiobjective knapsack problem which is known to be NP-complete. We use two well known simple functions LOTZ (Leading Zeros: Trailing Ones) and a quadratic function. For the knapsack problem we formalize a (1 + ε)-approximation set under a constraint on the weights of the items. We then generalize the idea by eliminating the constraints based on a principle of partitioning the items into blocks and analyze REMO on it. We use a simple strategy based on partitioning of the decision space into fitness layers for the analysis.
机译：在本文中，我们提出了一种基于受限交配池（REMO）的多目标进化算法，该算法带有单独的档案库来存储剩余种群。这种基于存档的算法已用于解决实际应用，但是，没有理论结果可用。在本文中，我们针对两个简单的离散伪布尔函数以及已知为NP完全的多目标背包问题，对该算法进行了严格的运行时间复杂度分析。我们使用两个众所周知的简单函数LOTZ（前导零：尾随一个）和一个二次函数。对于背包问题，我们在项权重的约束下将（1 +ε）近似集形式化。然后，我们基于将项目划分为块并在其上分析REMO的原理，通过消除约束来概括该想法。我们使用基于决策空间划分为适应性层的简单策略进行分析。

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