Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
Search space reduction technique for constrained optimization with tiny feasible space
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Novel Component-Based Model and Ranking Strategy in Constrained Evolutionary Optimization
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
A hybrid intelligent genetic algorithm
Advanced Engineering Informatics
Constraint handling in multiobjective evolutionary optimization
IEEE Transactions on Evolutionary Computation
An adaptive penalty formulation for constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An agent-based memetic algorithm (AMA) for nonlinear optimization with equality constraints
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Classification-Assisted Memetic Algorithms for Equality-Constrained Optimization Problems
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
A rough set penalty function for marriage selection in multiple-evaluation genetic algorithms
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
International Journal of Bio-Inspired Computation
Ensemble of constraint handling techniques
IEEE Transactions on Evolutionary Computation
A hybrid evolutionary approach to the nurse Rostering problem
IEEE Transactions on Evolutionary Computation
Feasibility structure modeling: an effective chaperone for constrained memetic algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
A new self-adaption differential evolution algorithm based component model
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
A new immune clone algorithm to solve the constrained optimization problems
WSEAS Transactions on Computers
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Inequality constraint handling in genetic algorithms using a boundary simulation method
Computers and Operations Research
Modified harmony search optimization for constrained design problems
Expert Systems with Applications: An International Journal
A penalty-based evolutionary algorithm for constrained optimization
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Preference bi-objective evolutionary algorithm for constrained optimization
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Expert Systems with Applications: An International Journal
Handling constraints in global optimization using an artificial immune system
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
An improved (µ+λ)-constrained differential evolution for constrained optimization
Information Sciences: an International Journal
Multistage covariance matrix adaptation with differential evolution for constrained optimization
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
A penalty function-based differential evolution algorithm for constrained global optimization
Computational Optimization and Applications
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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
Natural Computing: an international journal
| Hi-index | 0.00 |
A self-adaptive fitness formulation is presented for solving constrained optimization problems. In this method, the dimensionality of the problem is reduced by representing the constraint violations by a single infeasibility measure. The infeasibility measure is used to form a two-stage penalty that is applied to the infeasible solutions. The performance of the method has been examined by its application to a set of eleven test cases from the specialized literature. The results have been compared with previously published results from the literature. It is shown that the method is able to find the optimum solutions. The proposed method requires no parameter tuning and can be used as a fitness evaluator with any evolutionary algorithm. The approach is also robust in its handling of both linear and nonlinear equality and inequality constraint functions. Furthermore, the method does not require an initial feasible solution.