Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Multiple Vehicle Routing with Time and Capacity Constraints Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Risk-Resilient Heuristics and Genetic Algorithms for Security-Assured Grid Job Scheduling
IEEE Transactions on Computers
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Although successful at first, messy genetic algorithms had minimum attention within the evolutionary computation community for the past few years. This chapter presents an ordering messy genetic algorithm (OmeGA) that is able to solve difficult permutation problems efficiently. Starting with a brief introduction to the fast messy genetic algorithm (fmGA), the chapter continues by proposing a robust representation model--the random keys--that proved to work successfully for representing permutations. The design of OmeGA is described and ordering deceptive problems are discussed in detail. Thereafter, experimental results that show the random key-based simple genetic algorithm (RKGA) being outperformed by its messy competitor in 32-length ordering deceptive problems are presented. The OmeGA is completely independent from the underlying chromosome's coding scheme and finds the global optimal solution in problems with both tightly and loosely coded building blocks. The chapter finally demonstrates the OmeGA's scale-up behavior.