Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
The shifting bottleneck procedure for job shop scheduling
Management Science
An algorithm for solving the job-shop problem
Management Science
Job shop scheduling by simulated annealing
Operations Research
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A fast taboo search algorithm for the job shop problem
Management Science
Analysis of speciation and niching in the multi-niche crowding GA
Theoretical Computer Science - Special issue on evolutionary computation
An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Scheduling Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Problem difficulty for tabu search in job-shop scheduling
Artificial Intelligence
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Direct Chromosome Representation and Advanced Genetic Operators for Production Scheduling
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
An Efficient Genetic Algorithm for Job Shop Scheduling Problems
Proceedings of the 6th International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
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
Niche identification techniques in multimodal genetic search with sharing scheme
Advances in Engineering Software
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Evolutionary Scheduling: A Review
Genetic Programming and Evolvable Machines
An Advanced Tabu Search Algorithm for the Job Shop Problem
Journal of Scheduling
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
A sequential niche technique for multimodal function optimization
Evolutionary Computation
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
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In this paper the performance of the most recent multi-modal genetic algorithms (MMGAs) on the Job Shop Scheduling Problem (JSSP) is compared in term of efficacy, multi-solution based efficacy (the algorithm's capability to find multiple optima), and diversity in the final set of solutions. The capability of Genetic Algorithms (GAs) to work on a set of solutions allows us to reach different optima in only one run. Nevertheless, simple GAs are not able to maintain different solutions in the last iteration, therefore reaching only one local or global optimum. Research based on the preservation of the diversity through MMGAs has provided very promising results. These techniques, known as niching methods or MMGAs, allow not only to obtain different multiple global optima, but also to preserve useful diversity against convergence to only one solution (the usual behaviour of classical GAs). In previous works, a significant difference in the performance among methods was found, as well as the importance of a suitable parametrization. In this work classic methods are compared to the most recent MMGAs, grouped in three classes (sharing, clearing and species competition), for JSSP. Our experimental study found that those new MMGAs which have a certain type of replacement process perform much better (in terms of highest efficacy and multi-solution based efficacy) than classical MMGAs which do not have this type of process.