Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Scheduling Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Comparison of Genetic Algorithms for the Static Job Shop Scheduling Problem
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Local Search Genetic Algorithms for the Job Shop Scheduling Problem
Applied Intelligence
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
A comparative study of stochastic optimization methods in electric motor design
Applied Intelligence
A memetic algorithm for the job-shop with time-lags
Computers and Operations Research
An artificial intelligence approach to the efficiency improvement of a universal motor
Engineering Applications of Artificial Intelligence
Genetic algorithm for test pattern generator design
Applied Intelligence
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
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When dealing with real-world problems, it turns out that there are many specifics of the problem we are trying to solve. Since many algorithms that are being developed are evaluated and compared on test benchmark problems, they can simulate real-world problems up to some degree and specifics are not presumed and tested. To make algorithms efficient, such specifics need to be considered and included in the problem solving. In this paper, a real-world production scheduling problem is addressed. A typical approach with genetic algorithm turned out to be insufficient due to added complexity of many specifics. To successfully solve this problem, a memetic algorithm, which uses problem-specific local search procedures to improve solutions acquired by genetic algorithm, is proposed. It is shown that the use of such local search procedures can significantly improve the effectiveness and efficiency of the algorithm.