Localization Strategy for Island Model Genetic Algorithm to Preserve Population Diversity
Localization Strategy for Island Model Genetic Algorithm to Preserve Population Diversity
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DOI：
10.1007/978-3-319-60170-0_11
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发表时间：
2017-05
期刊：
影响因子：
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通讯作者：
A. A. Gozali-A.;S. Fujimura
中科院分区：
文献类型：
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作者：
A. A. Gozali-A.;S. Fujimura
遗传算法（Genetic Algorithm，GA）自Fraser提出并由Bremermann改进后，在解决各种优化问题方面取得了长足的进步。遗传算法也蓬勃发展到许多变化的模型和方法。多种群或孤岛模型遗传算法（IMGA）是一种常用的遗传算法模型。IMGA是一种多种群遗传算法模型，其目标是通过本质上保持其多样性来获得更好的结果（旨在获得全局最优）。IMGA的本地化策略是一种新的方法，它将岛屿视为其个体的单一生活环境。一个岛屿的特性，肯定和其他岛屿不同。运营商的参数配置甚至其核心引擎（算法）都代表着一个孤岛的性质。这些差异会导致其演化轨迹的不同，即演化速度的不同或演化模式的不同。IMGA的定位策略采用三种单GA核：标准GA、伪GA和知情GA。本地化策略实现迁移协议和偏移值来控制移动。实验结果表明，IMGA的定位策略成功地解决了3-SAT具有良好的性能。这种全新的方法也被证明具有高度的一致性和耐用性。
Years after being firstly introduced by Fraser and remodeled for modern application by Bremermann, genetic algorithm (GA) has a significant progression to solve many kinds of optimization problems. GA also thrives into many variations of models and approaches. Multi-population or island model GA (IMGA) is one of the commonly used GA models. IMGA is a multi-population GA model objected to getting a better result (aimed to get global optimum) by intrinsically preserve its diversity. Localization strategy of IMGA is a new approach which sees an island as a single living environment for its individuals. An island’s characteristic must be different compared to other islands. Operator parameter configuration or even its core engine (algorithm) represents the nature of an island. These differences will incline into different evolution tracks which can be its speed or pattern. Localization strategy for IMGA uses three kinds of single GA core: standard GA, pseudo GA, and informed GA. Localization strategy implements migration protocol and the bias value to control the movement. The experiment results showed that localization strategy for IMGA succeeds to solve 3-SAT with an excellent performance. This brand new approach is also proven to have a high consistency and durability.