Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Coevolutionary automated software correction
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Multi-objective coevolutionary automated software correction
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Current challenges in automatic software repair
Software Quality Control
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Fitness function design is often both a design and performance bottleneck for evolutionary algorithms. The fitness function for a given problem is directly related to the specifications for that problem. This paper outlines a guide for transforming problem specifications into a fitness function. The target audience for this guide are both non-expert practitioners and those interested in formalizing fitness function design. The goal is to investigate and formalize the fitness function generation process that expert developers go through and in doing so make fitness function design less of a bottleneck. Solution requirements in the problem specifications are identified and classified, then an appropriate fitness function component is generated based on its classifications, and finally the fitness function components combined to yield a fitness function for the problem in question. The competitive performance of a guide generated fitness function is demonstrated by comparing it to that of an expert designed fitness function.