A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
The single machine early/tardy problem
Management Science
Scheduling with release dates on a single machine to minimize total weighted completion time
Discrete Applied Mathematics
A greedy heuristic for bicriterion single machine scheduling problems
Computers and Industrial Engineering
One-machine rescheduling heuristics with efficiency and stability as criteria
Computers and Operations Research
Fast probabilistic modeling for combinatorial optimization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A branch-and-bound algorithm for the single machine earliness and tardiness scheduling problem
Computers and Operations Research
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Variable Neighborhood Decomposition Search
Journal of Heuristics
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
The One-Machine Problem with Earliness and Tardiness Penalties
Journal of Scheduling
Improved heuristics for the early/tardy scheduling problem with no idle time
Computers and Operations Research
Architecture for an Artificial Immune System
Evolutionary Computation
GA-EDA: hybrid evolutionary algorithm using genetic and estimation of distribution algorithms
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Design and Analysis of Experiments
Design and Analysis of Experiments
Expert Systems with Applications: An International Journal
Computers and Operations Research
The equation for response to selection and its use for prediction
Evolutionary Computation
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
A hybrid electromagnetism-like algorithm for single machine scheduling problem
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Bio-inspired and gradient-based algorithms to train MLPs: The influence of diversity
Information Sciences: an International Journal
Mutation matrix in evolutionary computation: an application to resource allocation problem
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
IEEE Transactions on Evolutionary Computation
A hybrid heuristic for the traveling salesman problem
IEEE Transactions on Evolutionary Computation
On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Extended artificial chromosomes genetic algorithm for permutation flowshop scheduling problems
Computers and Industrial Engineering
Computers and Industrial Engineering
A novel classification learning framework based on estimation of distribution algorithms
International Journal of Computing Science and Mathematics
Hi-index | 12.05 |
The goal of this research is to deduce important guidelines for designing effective Estimation of Distribution Algorithms (EDAs). These guidelines will enhance the designed algorithms in balancing the intensification and diversification effects of EDAs. Most EDAs have the advantage of incorporating probabilistic models which can generate chromosomes with the non-disruption of salient genes. This advantage, however, may cause the problem of the premature convergence of EDAs resulted in the probabilistic models no longer generating diversified solutions. In addition, due to overfitting of the search space, probabilistic models cannot really represent the general information of the population. Therefore, this research will deduce important guidelines through the convergency speed analysis of EDAs under different computational times for designing effective EDA algorithms. The major idea is to increase the population diversity gradually by hybridizing EDAs with other meta-heuristics and replacing the procedures of sampling new solutions. According to that, this research further proposes an Adaptive EA/G to improve the performance of EA/G. The proposed algorithm solves the single machine scheduling problems with earliness/tardiness cost in a just-in-time scheduling environment. The experimental results indicated that the Adaptive EA/G outperforms ACGA and EA/G statistically significant in different stopping criteria. This paper, hence, is of importance in the field of EDAs as well as for the researchers in studying the scheduling problems.