International Journal of Expert Systems
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
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
Artificial Intelligence Review
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
The equation for response to selection and its use for prediction
Evolutionary Computation
Connection Science - Evolutionary Learning and Optimisation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
Solving multimodal combinatorial puzzles with edge-based estimation of distribution algorithm
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Computers and Industrial Engineering
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Most optimization algorithms that use probabilistic models focus on extracting the information from good solutions found in the population. A selection method discards the below-average solutions. They do not contribute any information to be used to update the models. This work proposes a new algorithm, Combinatorial Optimization with Coincidence (COIN) that makes use of both good and not-good solutions. A Generator represents a probabilistic model of the required solution, is used to sample candidate solutions. Reward and punishment schemes are incorporated in updating the generator. The updating values are defined by selecting the good and not-good solutions. It has been observed that the not-good solutions contribute to avoid producing the bad solutions. The multi-objective version of COIN is also introduced. Several benchmarks of multi-objective problems of real world industrial applications are reported.