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Constraint Handling Guided by Landscape Analysis in Combinatorial and Continuous Search Spaces
Evolutionary Computation ( IF 6.8 ) Pub Date : 2019-06-01 , DOI: 10.1162/evco_a_00222
Katherine M Malan 1 , I Moser 2
Affiliation  

The notion and characterisation of fitness landscapes has helped us understand the performance of heuristic algorithms on complex optimisation problems. Many practical problems, however, are constrained, and when significant areas of the search space are infeasible, researchers have intuitively resorted to a variety of constraint-handling techniques intended to help the algorithm manoeuvre through infeasible areas and toward feasible regions of better fitness. It is clear that providing constraint-related feedback to the algorithm to influence its choice of solutions overlays the violation landscape with the fitness landscape in unpredictable ways whose effects on the algorithm cannot be directly measured. In this work, we apply metrics of violation landscapes to continuous and combinatorial problems to characterise them. We relate this information to the relative performance of six well-known constraint-handling techniques to demonstrate how some properties of constrained landscapes favour particular constraint-handling approaches. For the problems with sampled feasible solutions, a bi-objective approach was the best performing approach overall, but other techniques performed better on problems with the most disjoint feasible areas. For the problems with no measurable feasibility, a feasibility ranking approach was the best performing approach overall, but other techniques performed better when the correlation between fitness values and the level of constraint violation was high.

中文翻译:

在组合和连续搜索空间中以景观分析为指导的约束处理

适应度景观的概念和特征帮助我们理解启发式算法在复杂优化问题上的性能。然而,许多实际问题受到限制,并且当搜索空间的重要区域不可行时,研究人员直觉地求助于各种约束处理技术,旨在帮助算法通过不可行区域和更好适应度的可行区域进行机动。很明显,向算法提供与约束相关的反馈以影响其解决方案的选择会以不可预测的方式将违规景观与适应度景观叠加在一起,其对算法的影响无法直接测量。在这项工作中,我们将违规景观指标应用于连续和组合问题以表征它们。我们将此信息与六种众所周知的约束处理技术的相对性能联系起来,以展示受约束景观的某些属性如何有利于特定的约束处理方法。对于采样可行解的问题,双目标方法总体上是性能最好的方法,但其他技术在最不相交的可行区域的问题上表现更好。对于没有可测量可行性的问题,可行性排序方法总体上是性能最好的方法,但当适应度值与约束违反程度之间的相关性很高时,其他技术表现更好。对于采样可行解的问题,双目标方法总体上是性能最好的方法,但其他技术在最不相交的可行区域的问题上表现更好。对于没有可测量可行性的问题,可行性排序方法总体上是性能最好的方法,但当适应度值与约束违反程度之间的相关性很高时,其他技术表现更好。对于采样可行解的问题,双目标方法总体上是性能最好的方法,但其他技术在最不相交的可行区域的问题上表现更好。对于没有可测量可行性的问题,可行性排序方法总体上是性能最好的方法,但当适应度值与约束违反程度之间的相关性很高时,其他技术表现更好。
更新日期:2019-06-01
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