Information Sciences: an International Journal
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
A Constrained Dynamic Evolutionary Algorithm with Adaptive Penalty Coefficient
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Expert Systems with Applications: An International Journal
Proceedings of the first ACM/SIGEVO Summit 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
Constraint handling in multiobjective evolutionary optimization
IEEE Transactions on Evolutionary Computation
Dynamic multiple swarms in multiobjective particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An adaptive penalty formulation for constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Vaccine-enhanced artificial immune system for multimodal function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Comparison studies of LS_SVM and SVM on modeling for fermentation processes
ICNC'09 Proceedings of the 5th international conference on Natural computation
A heuristic-based framework to solve a complex aircraft sizing problem
Engineering Applications of Artificial Intelligence
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
An enhanced GA technique for system training and prognostics
Advances in Engineering Software
Multi-level ranking for constrained multi-objective evolutionary optimisation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Triangulation of bayesian networks using an adaptive genetic algorithm
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Constrained optimization based on modified differential evolution algorithm
Information Sciences: an International Journal
Journal of Electrical and Computer Engineering - Special issue on Applications of Heuristics and Metaheuristics in Power Systems
Empirical evaluation of search based requirements interaction management
Information and Software Technology
An improved (µ+λ)-constrained differential evolution for constrained optimization
Information Sciences: an International Journal
Facial Feature Tracking via Evolutionary Multiobjective Optimization
International Journal of Applied Evolutionary Computation
Constraint Handling in Particle Swarm Optimization
International Journal of Swarm Intelligence Research
A Multiobjective Particle Swarm Optimizer for Constrained Optimization
International Journal of Swarm Intelligence Research
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
Environmental Modelling & Software
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In this paper, we propose a generic, two-phase framework for solving constrained optimization problems using genetic algorithms. In the first phase of the algorithm, the objective function is completely disregarded and the constrained optimization problem is treated as a constraint satisfaction problem. The genetic search is directed toward minimizing the constraint violation of the solutions and eventually finding a feasible solution. A linear rank-based approach is used to assign fitness values to the individuals. The solution with the least constraint violation is archived as the elite solution in the population. In the second phase, the simultaneous optimization of the objective function and the satisfaction of the constraints are treated as a biobjective optimization problem. We elaborate on how the constrained optimization problem requires a balance of exploration and exploitation under different problem scenarios and come to the conclusion that a nondominated ranking between the individuals will help the algorithm explore further, while the elitist scheme will facilitate in exploitation. We analyze the proposed algorithm under different problem scenarios using Test Case Generator-2 and demonstrate the proposed algorithm's capability to perform well independent of various problem characteristics. In addition, the proposed algorithm performs competitively with the state-of-the-art constraint optimization algorithms on 11 test cases which were widely studied benchmark functions in literature.