论文标题
结合遗传编程和粒子群优化以简化崎landscapes的探索
Combining Genetic Programming and Particle Swarm Optimization to Simplify Rugged Landscapes Exploration
论文作者
论文摘要
传统的统计技术或元启发式学很难解决大多数现实世界的优化问题。主要困难与存在相当数量的局部Optima有关，这可能导致优化过程的过早收敛性。为了解决这个问题，我们提出了一种新型的启发式方法，用于构建原始功能的平滑替代模型。替代功能更容易优化，但保持原始坚固的健身景观的基本属性：全球最佳的位置。为了创建这样的替代模型，我们考虑通过自我调整的健身函数增强的线性遗传编程方法。所提出的称为GP-FST-PSO替代模型的算法在搜索全局最优值和产生原始基准函数的视觉近似（在二维情况下）的搜索中取得了令人满意的结果。
Most real-world optimization problems are difficult to solve with traditional statistical techniques or with metaheuristics. The main difficulty is related to the existence of a considerable number of local optima, which may result in the premature convergence of the optimization process. To address this problem, we propose a novel heuristic method for constructing a smooth surrogate model of the original function. The surrogate function is easier to optimize but maintains a fundamental property of the original rugged fitness landscape: the location of the global optimum. To create such a surrogate model, we consider a linear genetic programming approach enhanced by a self-tuning fitness function. The proposed algorithm, called the GP-FST-PSO Surrogate Model, achieves satisfactory results in both the search for the global optimum and the production of a visual approximation of the original benchmark function (in the 2-dimensional case).
