Journal of Global Optimization
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Fuzzy Recombination for the Breeder Genetic Algorithm
Proceedings of the 6th International Conference on Genetic Algorithms
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A line up evolutionary algorithm for solving nonlinear constrained optimization problems
Computers and Operations Research
Search space reduction technique for constrained optimization with tiny feasible space
Proceedings of the 10th annual conference on Genetic and evolutionary computation
An analysis on crossovers for real number chromosomes in an infinite population size
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Differential evolution algorithm with strategy adaptation for global numerical 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
Varying number of difference vectors in differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A differential evolution algorithm with self-adapting strategy and control parameters
Computers and Operations Research
Differential evolution algorithm with ensemble of parameters and mutation strategies
Applied Soft Computing
A three-strategy based differential evolution algorithm for constrained optimization
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
A comparative study of different variants of genetic algorithms for constrained optimization
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Solving rotated multi-objective optimization problems using differential evolution
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
An empirical study on the synergy of multiple crossover operators
IEEE Transactions on Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
An Adaptive Tradeoff Model for Constrained Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
On an evolutionary approach for constrained optimization problem solving
Applied Soft Computing
Multi-operator based biogeography based optimization with mutation for global numerical optimization
Computers & Mathematics with Applications
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Self-adaptive differential evolution incorporating a heuristic mixing of operators
Computational Optimization and Applications
Computers and Electronics in Agriculture
Differential evolution with multi-constraint consensus methods for constrained optimization
Journal of Global Optimization
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Over the last two decades, many sophisticated evolutionary algorithms have been introduced for solving constrained optimization problems. Due to the variability of characteristics in different COPs, no single algorithm performs consistently over a range of problems. In this paper, for a better coverage of the problem characteristics, we introduce an algorithm framework that uses multiple search operators in each generation. The appropriate mix of the search operators, for any given problem, is determined adaptively. The framework is tested by implementing two different algorithms. The performance of the algorithms is judged by solving 60 test instances taken from two constrained optimization benchmark sets from specialized literature. The first algorithm, which is a multi-operator based genetic algorithm (GA), shows a significant improvement over different versions of GA (each with a single one of these operators). The second algorithm, using differential evolution (DE), also confirms the benefit of the multi-operator algorithm by providing better and consistent solutions. The overall results demonstrated that both GA and DE based algorithms show competitive, if not better, performance as compared to the state of the art algorithms.