Information Sciences—Applications: An International Journal
Inference of a gene regulatory network by means of interactive evolutionary computing
Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
A Multi-objective Approach to Constrained Optimisation of Gas Supply Networks: the COMOGA Method
Selected Papers from AISB Workshop on Evolutionary Computing
Optimization with constraints using a cultured differential evolution approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Constrained optimization via particle evolutionary swarm optimization algorithm (PESO)
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Robust watermarking and compression for medical images based on genetic algorithms
Information Sciences: an International Journal
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
A Generic Framework for Constrained Optimization Using Genetic Algorithms
IEEE Transactions on Evolutionary Computation
An ontology-based approach to learnable focused crawling
Information Sciences: an International Journal
A Novel Particle Swarm Optimization for Constrained Engineering Optimization Problems
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization
Information Sciences: an International Journal
Approximating Pareto frontier using a hybrid line search approach
Information Sciences: an International Journal
Differential evolution in constrained numerical optimization: An empirical study
Information Sciences: an International Journal
Region-Reduction Division Criteria-based hybrid constrained optimisation
International Journal of Artificial Intelligence and Soft Computing
A novel selection evolutionary strategy for constrained optimization
Information Sciences: an International Journal
A rough penalty genetic algorithm for constrained optimization
Information Sciences: an International Journal
Hi-index | 0.07 |
One of the most important issues in developing an evolutionary optimization algorithm is the proper handling of any constraints on the problem. One must balance the objective function against the degree of constraint violation in such a way that neither is dominant. Common approaches to strike this balance include implementing a penalty function and the more recent stochastic ranking method, but these methods require an extra tuning parameter which must be chosen by the user. The present paper demonstrates that a proper balance can be achieved using an addition of ranking method. Through the ranking of the individuals with respect to the objective function and constraint violation independently, we convert these two properties into numerical values of the same order of magnitude. This removes the requirement of a user-specified penalty coefficient or any other tuning parameters. Direct addition of the ranking terms is then performed to integrate all information into a single decision variable. This approach is incorporated into a (@m,@l) evolution strategy and tested on thirteen benchmark problems, one engineering design problem, and five difficult problems with a high dimensionality or many constraints. The performance of the proposed strategy is similar to that of the stochastic ranking method when dealing with inequality constraints, but it has a much simpler ranking procedure and does not require any tuning parameters. A percentage-based tolerance value adjustment scheme is also proposed to enable feasible search when dealing with equality constraints.