离散域的约束驱动求解模型：以考试时间表问题为例
A Constraint Driven Solution Model for Discrete Domains with a Case Study of Exam Timetabling Problems
AI总结：
本文提出约束驱动进化算法ICHEA求解离散域考试时间表问题，通过联姻交叉算子增量满足约束并结合传统与增强算子优化解，提出无需问题依赖适应度函数的通用偏好求解模型。
摘要：
许多科学与工程应用需要通过满足一组约束来寻找规划与优化问题的解。这些约束问题（CPs）通常是NP完全的，可形式化为约束满足问题（CSPs）或约束优化问题（COPs）。进化算法（EAs）是各类问题域中无处不在的优化问题的优秀求解器，然而传统的EA算子对约束是“盲目的”，或者通常使用依赖于问题的目标函数；因为它们在搜索解时不利用约束中的信息。EA的一种变体，智能约束处理进化算法（ICHEA），在我们早期的工作（Sharma和Sharma，2012）中已被证明是用于连续约束问题的通用约束引导EA，它从约束中提取信息并在进化搜索中加以利用，使搜索更高效。本文展示了ICHEA求解基准考试时间表问题，这是一个经典的COP。所提出的方法在解的质量方面与其他最先进的EA方法展示了有竞争力的结果。ICHEA首先使用其联姻交叉算子增量地满足所有给定约束，然后使用传统算子与增强算子的组合来优化解。通常，由EA求解的CPs是依赖于问题的基于惩罚的适应度函数。我们还提出了一种通用的基于偏好的求解模型，它不需要依赖于问题的适应度函数，但目前它仅适用于互斥约束。
英文摘要：
Many science and engineering applications require finding solutions to planning and optimization problems by satisfying a set of constraints. These constraint problems (CPs) are typically NP-complete and can be formalized as constraint satisfaction problems (CSPs) or constraint optimization problems (COPs). Evolutionary algorithms (EAs) are good solvers for optimization problems ubiquitous in various problem domains, however traditional operators for EAs are 'blind' to constraints or generally use problem dependent objective functions; as they do not exploit information from the constraints in search for solutions. A variation of EA, Intelligent constraint handling evolutionary algorithm (ICHEA), has been demonstrated to be a versatile constraints-guided EA for continuous constrained problems in our earlier works in (Sharma and Sharma, 2012) where it extracts information from constraints and exploits it in the evolutionary search to make the search more efficient. In this paper ICHEA has been demonstrated to solve benchmark exam timetabling problems, a classic COP. The presented approach demonstrates competitive results with other state-of-the-art approaches in EAs in terms of quality of solutions. ICHEA first uses its inter-marriage crossover operator to satisfy all the given constraints incrementally and then uses combination of traditional and enhanced operators to optimize the solution. Generally CPs solved by EAs are problem dependent penalty based fitness functions. We also proposed a generic preference based solution model that does not require a problem dependent fitness function, however currently it only works for mutually exclusive constraints.
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。
