Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Subcost-Guided Search—Experiments with Timetabling Problems
Journal of Heuristics
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Do additional objectives make a problem harder?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary algorithms and multi-objectivization for the travelling salesman problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Guiding single-objective optimization using multi-objective methods
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Helper-objective optimization strategies for the Job-Shop Scheduling Problem
Applied Soft Computing
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A Multiple Objective Evolutionary Algorithm (MOEA) applied to the Job-Shop Scheduling Problem has been shown in the past to perform better than a single objective Genetic Algorithm (GA). Helper-objectives, representing portions of the main objective, were used in the past to guide the MOEA search process. This paper explores additional understanding of helper-objective sequencing. The sequence in which helper-objectives are used is examined and it is shown that problem specific knowledge can be incorporated to determine a good helper-objective sequence. Results demonstrate how carefully sequenced helper-objectives can improve search quality. Explanations are provided for how helpers accelerate the search process by distinguishing between otherwise similar solutions and by partial removal of epistasis in one or more dimensions. Good helper-objective sequence appears to break epistasis early in a search which implies that it is important for helper-objective methods to examine the sequence of objectives.